{"total":77,"rows":[{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"area":null},{"id":"2102.07053","title":"Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients","created":"2021-02-14","cited_by_count":123,"area":null},{"id":"2112.11256","title":"Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling","created":"2021-12-21","cited_by_count":66,"area":null},{"id":"2103.14272","title":"Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design","created":"2021-03-26","cited_by_count":47,"area":null},{"id":"2205.13648","title":"A Unified Analysis of Federated Learning with Arbitrary Client Participation","created":"2022-05-26","cited_by_count":45,"area":null},{"id":"2110.10302","title":"Layer-wise Adaptive Model Aggregation for Scalable Federated Learning","created":"2021-10-19","cited_by_count":36,"area":null},{"id":"2209.00230","title":"Trading Off Privacy, Utility and Efficiency in Federated Learning","created":"2022-09-01","cited_by_count":33,"area":null},{"id":"2111.01108","title":"Resource-Efficient Federated Learning","created":"2021-11-01","cited_by_count":26,"area":null},{"id":"2105.09080","title":"Accelerating Gossip SGD with Periodic Global Averaging","created":"2021-05-19","cited_by_count":25,"area":null},{"id":"2109.05411","title":"Cost-Effective Federated Learning in Mobile Edge Networks","created":"2021-09-12","cited_by_count":25,"area":null},{"id":"2310.08670","title":"Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction","created":"2023-10-12","cited_by_count":24,"area":null},{"id":"2204.13169","title":"FedShuffle: Recipes for Better Use of Local Work in Federated Learning","created":"2022-04-27","cited_by_count":21,"area":null},{"id":"2110.08133","title":"Trade-offs of Local SGD at Scale: An Empirical Study","created":"2021-10-15","cited_by_count":20,"area":null},{"id":"2106.04759","title":"Communication-efficient SGD: From Local SGD to One-Shot Averaging","created":"2021-06-09","cited_by_count":19,"area":null},{"id":"2105.11028","title":"Fast Federated Learning by Balancing Communication Trade-Offs","created":"2021-05-23","cited_by_count":18,"area":null},{"id":"2210.16402","title":"GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity","created":"2022-10-28","cited_by_count":18,"area":null},{"id":"2103.01319","title":"Adversarial training in communication constrained federated learning","created":"2021-03-01","cited_by_count":17,"area":null},{"id":"2104.05245","title":"Distributed Learning Systems with First-order Methods","created":"2021-04-12","cited_by_count":17,"area":null},{"id":"2111.05897","title":"Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters","created":"2021-11-10","cited_by_count":17,"area":null},{"id":"2105.07806","title":"Towards Demystifying Serverless Machine Learning Training","created":"2021-05-17","cited_by_count":16,"area":null},{"id":"2108.00365","title":"A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee","created":"2021-08-01","cited_by_count":16,"area":null},{"id":"2105.07320","title":"LocalNewton: Reducing Communication Bottleneck for Distributed Learning","created":"2021-05-16","cited_by_count":15,"area":null},{"id":"2105.01281","title":"Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX","created":"2021-05-04","cited_by_count":14,"area":null},{"id":"2212.08272","title":"Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization","created":"2022-12-16","cited_by_count":14,"area":null},{"id":"2107.01499","title":"BAGUA: Scaling up Distributed Learning with System Relaxations","created":"2021-07-03","cited_by_count":13,"area":null},{"id":"2112.10313","title":"Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity","created":"2021-12-20","cited_by_count":12,"area":null},{"id":"2212.08496","title":"Federated Learning with Flexible Control","created":"2022-12-16","cited_by_count":12,"area":null},{"id":"2108.06869","title":"FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning","created":"2021-08-16","cited_by_count":11,"area":null},{"id":"2210.00690","title":"Taming Fat-Tailed (\"Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning","created":"2022-10-03","cited_by_count":10,"area":null},{"id":"2404.06114","title":"Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey","created":"2024-04-09","cited_by_count":10,"area":null},{"id":"2102.03970","title":"Coordinating Momenta for Cross-silo Federated Learning","created":"2021-02-08","cited_by_count":9,"area":null},{"id":"2404.13804","title":"Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks","created":"2024-04-22","cited_by_count":9,"area":null},{"id":"2109.05613","title":"Critical Learning Periods in Federated Learning","created":"2021-09-12","cited_by_count":8,"area":null},{"id":"2201.03789","title":"Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits","created":"2022-01-11","cited_by_count":8,"area":null},{"id":"2301.10977","title":"Time-sensitive Learning for Heterogeneous Federated Edge Intelligence","created":"2023-01-26","cited_by_count":8,"area":null},{"id":"2209.05148","title":"Personalized Federated Learning with Communication Compression","created":"2022-09-12","cited_by_count":6,"area":null},{"id":"2207.04330","title":"Multi-Model Federated Learning with Provable Guarantees","created":"2022-07-09","cited_by_count":5,"area":null},{"id":"2310.14423","title":"A Quadratic Synchronization Rule for Distributed Deep Learning","created":"2023-10-22","cited_by_count":5,"area":null},{"id":"2311.07324","title":"Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing","created":"2023-11-13","cited_by_count":5,"area":null},{"id":"2406.18156","title":"FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization","created":"2024-06-26","cited_by_count":5,"area":null},{"id":"2410.03529","title":"No Need to Talk: Asynchronous Mixture of Language Models","created":"2024-10-04","cited_by_count":5,"area":null},{"id":"2107.08873","title":"RingFed: Reducing Communication Costs in Federated Learning on Non-IID Data","created":"2021-07-19","cited_by_count":4,"area":null},{"id":"2201.11803","title":"On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning","created":"2022-01-27","cited_by_count":4,"area":null},{"id":"2210.00665","title":"Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms","created":"2022-10-03","cited_by_count":4,"area":null},{"id":"2305.09628","title":"Faster Federated Learning with Decaying Number of Local SGD Steps","created":"2023-05-16","cited_by_count":4,"area":null},{"id":"2406.13936","title":"Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods","created":"2024-06-20","cited_by_count":4,"area":null},{"id":"2203.06798","title":"The Role of Local Steps in Local SGD","created":"2022-03-14","cited_by_count":2,"area":null},{"id":"2210.03521","title":"STSyn: Speeding Up Local SGD with Straggler-Tolerant Synchronization","created":"2022-10-06","cited_by_count":2,"area":null},{"id":"2211.02106","title":"Federated Hypergradient Descent","created":"2022-11-03","cited_by_count":2,"area":null},{"id":"2212.13679","title":"CC-FedAvg: Computationally Customized Federated Averaging","created":"2022-12-28","cited_by_count":2,"area":null},{"id":"2308.09187","title":"Distributed Extra-gradient with Optimal Complexity and Communication Guarantees","created":"2023-08-17","cited_by_count":2,"area":null},{"id":"2310.16106","title":"Decentralized Learning over Wireless Networks with Broadcast-Based Subgraph Sampling","created":"2023-10-24","cited_by_count":2,"area":null},{"id":"2312.13795","title":"Sparse Training for Federated Learning with Regularized Error Correction","created":"2023-12-21","cited_by_count":2,"area":null},{"id":"2403.02347","title":"On the Convergence of Federated Learning Algorithms without Data Similarity","created":"2024-02-29","cited_by_count":2,"area":null},{"id":"2408.04705","title":"Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks","created":"2024-08-08","cited_by_count":2,"area":null},{"id":"2202.08261","title":"Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation","created":"2022-02-16","cited_by_count":1,"area":null},{"id":"2304.05127","title":"Balancing Privacy and Performance for Private Federated Learning Algorithms","created":"2023-04-11","cited_by_count":1,"area":null},{"id":"2306.03401","title":"A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging","created":"2023-06-06","cited_by_count":1,"area":null},{"id":"2306.07497","title":"GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems","created":"2023-06-13","cited_by_count":1,"area":null},{"id":"2311.11083","title":"ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation","created":"2023-11-18","cited_by_count":1,"area":null},{"id":"2401.03083","title":"Energy-efficient Decentralized Learning via Graph Sparsification","created":"2024-01-05","cited_by_count":1,"area":null},{"id":"2405.20988","title":"Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging","created":"2024-05-31","cited_by_count":1,"area":null},{"id":"2411.06770","title":"Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis","created":"2024-11-11","cited_by_count":1,"area":null},{"id":"2512.24069","title":"Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning","created":"2025-12-30","cited_by_count":1,"area":null},{"id":"2608.06046","title":"ML-for-ML","created":"2026-08-06","cited_by_count":1,"area":null},{"id":"2206.11448","title":"Efficient Adaptive Federated Optimization of Federated Learning for IoT","created":"2022-06-23","cited_by_count":0,"area":null},{"id":"2412.07210","title":"EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models","created":"2024-12-10","cited_by_count":0,"area":null},{"id":"2504.12210","title":"Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks","created":"2025-04-16","cited_by_count":0,"area":null},{"id":"2505.04535","title":"FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules","created":"2025-05-07","cited_by_count":0,"area":null},{"id":"2507.04327","title":"TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments","created":"2025-07-06","cited_by_count":0,"area":null},{"id":"2507.17426","title":"Information Entropy-Based Scheduling for Communication-Efficient Decentralized Learning","created":"2025-07-23","cited_by_count":0,"area":null},{"id":"2507.20424","title":"Communication-Efficient Distributed Training for Collaborative Flat Optima Recovery in Deep Learning","created":"2025-07-27","cited_by_count":0,"area":null},{"id":"2512.17986","title":"FedOAED: Federated On-Device Autoencoder Denoiser for Heterogeneous Data under Limited Client Availability","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2601.22204","title":"FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2604.15549","title":"Optimizing Stochastic Gradient Push under Broadcast Communications","created":"2026-04-16","cited_by_count":0,"area":null},{"id":"2607.06979","title":"Robust Federated Learning Under Real-World Client Churn","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2609.36662","title":"AutoLoCo: Communication Efficient Distributed LLM Training via Adaptive Synchronization","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}