{"total":21,"rows":[{"id":"2307.10616","title":"Heterogeneous Federated Learning: State-of-the-art and Research Challenges","created":"2023-07-20","cited_by_count":161,"area":null},{"id":"2307.09619","title":"Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning","created":"2023-07-18","cited_by_count":29,"area":null},{"id":"2306.09104","title":"On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation","created":"2023-06-15","cited_by_count":16,"area":null},{"id":"2402.12780","title":"Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates","created":"2024-02-20","cited_by_count":14,"area":null},{"id":"2309.05505","title":"Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning","created":"2023-09-11","cited_by_count":13,"area":null},{"id":"2402.07002","title":"Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off","created":"2024-02-10","cited_by_count":7,"area":null},{"id":"2302.08044","title":"Balancing Privacy Protection and Interpretability in Federated Learning","created":"2023-02-16","cited_by_count":6,"area":null},{"id":"2409.20329","title":"Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients","created":"2024-09-30","cited_by_count":5,"area":null},{"id":"2311.18190","title":"Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning","created":"2023-11-30","cited_by_count":4,"area":null},{"id":"2308.04755","title":"Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data","created":"2023-08-09","cited_by_count":1,"area":null},{"id":"2406.16193","title":"Semi-Variance Reduction for Fair Federated Learning","created":"2024-06-23","cited_by_count":1,"area":null},{"id":"2410.08934","title":"Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees","created":"2024-10-11","cited_by_count":1,"area":null},{"id":"2409.13645","title":"DP$^2$-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization","created":"2024-09-20","cited_by_count":0,"area":null},{"id":"2412.17692","title":"FedTLU: Federated Learning with Targeted Layer Updates","created":"2024-12-23","cited_by_count":0,"area":null},{"id":"2503.09192","title":"Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates","created":"2025-03-12","cited_by_count":0,"area":null},{"id":"2503.20768","title":"An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy","created":"2025-03-26","cited_by_count":0,"area":null},{"id":"2505.14081","title":"Personalized and Resilient Distributed Learning Through Opinion Dynamics","created":"2025-05-20","cited_by_count":0,"area":null},{"id":"2510.20157","title":"ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push","created":"2025-10-23","cited_by_count":0,"area":null},{"id":"2601.00785","title":"FedHypeVAE: Federated Learning with Hypernetwork Generated Conditional VAEs for Differentially Private Embedding Sharing","created":"2026-01-02","cited_by_count":0,"area":null},{"id":"2602.22633","title":"Tackling Privacy Heterogeneity in Differentially Private Federated Learning","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2604.07125","title":"Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation","created":"2026-04-08","cited_by_count":0,"area":null}],"next_cursor":null}