{"total":32,"rows":[{"id":"2404.15381","title":"Advances and Open Challenges in Federated Foundation Models","created":"2024-04-23","cited_by_count":36,"area":null},{"id":"2401.04472","title":"A Survey on Efficient Federated Learning Methods for Foundation Model Training","created":"2024-01-09","cited_by_count":29,"area":null},{"id":"2406.05233","title":"Federated LoRA with Sparse Communication","created":"2024-06-07","cited_by_count":26,"area":null},{"id":"2407.19389","title":"FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction","created":"2024-07-28","cited_by_count":21,"area":null},{"id":"2306.10015","title":"Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness","created":"2023-06-16","cited_by_count":18,"area":null},{"id":"2306.04924","title":"Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation","created":"2023-06-08","cited_by_count":9,"area":null},{"id":"2406.00431","title":"SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead","created":"2024-06-01","cited_by_count":9,"area":null},{"id":"2408.03215","title":"FedBAT: Communication-Efficient Federated Learning via Learnable Binarization","created":"2024-08-06","cited_by_count":9,"area":null},{"id":"2306.12625","title":"Adaptive Compression in Federated Learning via Side Information","created":"2023-06-22","cited_by_count":6,"area":null},{"id":"2310.13033","title":"LASER: Linear Compression in Wireless Distributed Optimization","created":"2023-10-19","cited_by_count":6,"area":null},{"id":"2505.23176","title":"The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning","created":"2025-05-29","cited_by_count":5,"area":null},{"id":"2405.02341","title":"Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy","created":"2024-05-02","cited_by_count":4,"area":null},{"id":"2408.03220","title":"Masked Random Noise for Communication Efficient Federated Learning","created":"2024-08-06","cited_by_count":4,"area":null},{"id":"2404.15943","title":"Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme","created":"2024-04-24","cited_by_count":3,"area":null},{"id":"2410.08457","title":"Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients","created":"2024-10-11","cited_by_count":3,"area":null},{"id":"2410.14754","title":"On the Sparsity of the Strong Lottery Ticket Hypothesis","created":"2024-10-18","cited_by_count":3,"area":null},{"id":"2503.08085","title":"PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models","created":"2025-03-11","cited_by_count":3,"area":null},{"id":"2311.17299","title":"Federated Fine-Tuning of Foundation Models via Probabilistic Masking","created":"2023-11-29","cited_by_count":2,"area":null},{"id":"2410.03042","title":"FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning","created":"2024-10-03","cited_by_count":2,"area":null},{"id":"2309.16825","title":"A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets","created":"2023-09-28","cited_by_count":1,"area":null},{"id":"2401.08327","title":"Learn What You Need in Personalized Federated Learning","created":"2024-01-16","cited_by_count":1,"area":null},{"id":"2405.09037","title":"SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning","created":"2024-05-15","cited_by_count":1,"area":null},{"id":"2410.01309","title":"Getting Free Bits Back from Rotational Symmetries in LLMs","created":"2024-10-02","cited_by_count":1,"area":null},{"id":"2411.01759","title":"Automatic Structured Pruning for Efficient Architecture in Federated Learning","created":"2024-11-04","cited_by_count":1,"area":null},{"id":"2509.10161","title":"FedBiF: Communication-Efficient Federated Learning via Bits Freezing","created":"2025-09-12","cited_by_count":1,"area":null},{"id":"2309.10834","title":"Communication-Efficient Federated Learning via Regularized Sparse Random Networks","created":"2023-09-19","cited_by_count":0,"area":null},{"id":"2405.20623","title":"Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning","created":"2024-05-31","cited_by_count":0,"area":null},{"id":"2408.07075","title":"UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks","created":"2024-07-29","cited_by_count":0,"area":null},{"id":"2509.14603","title":"Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking","created":"2025-09-18","cited_by_count":0,"area":null},{"id":"2511.19959","title":"ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2512.19777","title":"Learned Digital Codes for Over-the-Air Computation in Federated Edge Learning","created":"2025-12-22","cited_by_count":0,"area":null},{"id":"2604.11278","title":"Representation-Aligned Multi-Scale Personalization for Federated Learning","created":"2026-04-13","cited_by_count":0,"area":null}],"next_cursor":null}