diff --git a/json_data/2024/main/MLSP-L2.json b/json_data/2024/main/MLSP-L2.json
new file mode 100644
index 0000000..eef9c65
--- /dev/null
+++ b/json_data/2024/main/MLSP-L2.json
@@ -0,0 +1,506 @@
+[
+  {
+    "title": "G2G: Generalized Learning by Cross-Domain Knowledge Transfer for Federated Domain Generalization",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447043",
+    "github": "NKU-EmbeddedSystem/G2G",
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Enhanced Unsupervised Domain Adaptation with Dual-Attention between Classification and Domain Alignment",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446867",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Elevating Visual Prompting in Transfer Learning via Pruned Model Ensembles: No Retrain, no Pain",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447808",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "IRLSG: Invariant Representation Learning for Single-Domain Generalization in Medical Image Segmentation",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446700",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Deep Neural Network Models Trained with a Fixed Random Classifier Transfer Better Across Domains",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447986",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2402.18614",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Enhancing Cross-Domain Detection: Adaptive Class-Aware Contrastive Transformer",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447182",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2401.13264",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Prompting to Prompt for Rehearsal-Free Class Incremental Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447170",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Trusted Deep Domain Adaptation with Uncertainty Measure based on Evidence Theory",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446542",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447236",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2309.07670",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Target Optimization Direction Guided Transfer Learning for Image Classification",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447810",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "PECR: Parameter-Efficient Transfer Learning with Cross-Modal Representation Learning for Remote Sensing Visual Question Answerin",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446146",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Cross-Lingual Learning in Multilingual Scene Text Recognition",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10445946",
+    "github": "ku21fan/CLL-STR",
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2312.10806",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Adversarial Domain Adaptation for Classification with Nested Dichotomies",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447352",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "INCPrompt: Task-Aware Incremental Prompting for Rehearsal-Free Class-Incremental Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447725",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2401.11667",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Source-Free Online Domain Adaptive Semantic Segmentation of Satellite Images Under Image Degradation",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447965",
+    "github": null,
+    "web_page": null,
+    "github_page": "https://sat-tta.github.io/",
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2401.02113",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Diversity-Aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-Time Adaptation",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10448449",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2401.00989",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "SGT: Self-Guided Transformer for Few-Shot Semantic Segmentation",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447595",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  },
+  {
+    "title": "Source-Free Domain Adaptation for Millimeter Wave Radar based Human Activity Recognition",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446797",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Transfer Learning"
+  }
+]
\ No newline at end of file
diff --git a/json_data/2024/main/SPCOM.json b/json_data/2024/main/SPCOM.json
index 9975c45..04e39f8 100644
--- a/json_data/2024/main/SPCOM.json
+++ b/json_data/2024/main/SPCOM.json
@@ -166,5 +166,761 @@
     "onedrive": null,
     "loom": null,
     "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Towards Resource-Efficient and Secure Federated Multimedia Recommendation",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10448131",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "FAVANO: Federated Averaging with Asynchronous Nodes",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446255",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2305.16099",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447174",
+    "github": "FederatedML/FedCompress",
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2401.14211",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Topology-Dependent Privacy Bound for Decentralized Federated Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446209",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2312.07956",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Federated PAC-Bayesian Learning on Non-IID Data",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446175",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2309.06683",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "MISA: Unveiling the Vulnerabilities in Split Federated Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10448225",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2312.11026",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Prompting Label Efficiency in Federated Graph Learning via Personalized Semi-Supervision",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10448483",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Towards Building the Federatedgpt: Federated Instruction Tuning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447454",
+    "github": "JayZhang42/FederatedGPT-Shepherd",
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Distributed Stochastic Contextual Bandits for Protein Drug Interaction",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447230",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Differentially Private Federated Frank-Wolfe",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447559",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Federated Learning on Distributed Graphs Considering Multiple Heterogeneities",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447691",
+    "github": "BaiqiLi123/ProtoFGL",
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "A Stochastic Gradient Approach for Communication Efficient Confederated Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10448035",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2402.18018",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "UNIDEAL: Curriculum Knowledge Distillation Federated Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447769",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2309.08961",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Federated CINN Clustering for Accurate Clustered Federated Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447282",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2309.01515",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Fairness-Aware Job Scheduling for Multi-Job Federated Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447084",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2401.02740",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Personalized Federated Learning with Attention-based Client Selection",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447362",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2312.15148",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Importance Sampling based Federated Unsupervised Representation Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447119",
+    "github": "nazreenshah/IS-FedVAE",
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Communication Efficient Private Federated Learning Using Dithering",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446222",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2309.07809",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "FedAQT: Accurate Quantized Training with Federated Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10445824",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Disentangle Estimation of Causal Effects from Cross-Silo Data",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446298",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": "2401.02154",
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "3D Parallelism for Transformers via Integer Programming",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446916",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Personalized Local Differentially Private Federated Learning with Adaptive Client Sampling",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447542",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "AdaFL: Adaptive Client Selection and Dynamic Contribution Evaluation for Efficient Federated Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447356",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Privacy Preserving Federated Learning from Multi-Input Functional Proxy Re-Encryption",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10446283",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Communication-Efficient Decentralized Dynamic Kernel Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10448365",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Tree Network Design for Faster Distributed Machine Learning Process with Distributed Dual Coordinate Ascent",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447341",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
+  },
+  {
+    "title": "Mutual Information based Noise Scale Optimization for Gradient Leakage Resistant Federated Learning",
+    "base_url": null,
+    "title_page": null,
+    "ieee_id": "10447749",
+    "github": null,
+    "web_page": null,
+    "github_page": null,
+    "colab": null,
+    "modelscope": null,
+    "gitee": null,
+    "gitlab": null,
+    "zenodo": null,
+    "kaggle": null,
+    "demo_page": null,
+    "paper_thecvf": null,
+    "paper_arxiv_id": null,
+    "paper_pdf": null,
+    "paper_hal_science": null,
+    "paper_researchgate": null,
+    "paper_amazon": null,
+    "youtube_id": null,
+    "drive_google": null,
+    "dropbox": null,
+    "onedrive": null,
+    "loom": null,
+    "section": "Distributed and Federated Learning"
   }
 ]
\ No newline at end of file