{"total":84,"rows":[{"id":"2111.04263","title":"Federated Learning Based on Dynamic Regularization","created":"2021-11-08","cited_by_count":562,"area":null},{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"area":null},{"id":"2108.06098","title":"FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning","created":"2021-08-13","cited_by_count":141,"area":null},{"id":"2202.09357","title":"ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!","created":"2022-02-18","cited_by_count":134,"area":null},{"id":"2106.10874","title":"FedCM: Federated Learning with Client-level Momentum","created":"2021-06-21","cited_by_count":77,"area":null},{"id":"2110.14216","title":"What Do We Mean by Generalization in Federated Learning?","created":"2021-10-27","cited_by_count":62,"area":null},{"id":"2103.05032","title":"Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning","created":"2021-03-08","cited_by_count":52,"area":null},{"id":"2208.11311","title":"Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments","created":"2022-08-24","cited_by_count":49,"area":null},{"id":"2102.13152","title":"Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency","created":"2021-02-25","cited_by_count":40,"area":null},{"id":"2111.03741","title":"Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective","created":"2021-11-05","cited_by_count":38,"area":null},{"id":"2103.03239","title":"Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices","created":"2021-03-04","cited_by_count":36,"area":null},{"id":"2102.01583","title":"The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication","created":"2021-02-02","cited_by_count":31,"area":null},{"id":"2106.09779","title":"Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses","created":"2021-06-17","cited_by_count":30,"area":null},{"id":"2203.02645","title":"Acceleration of Federated Learning with Alleviated Forgetting in Local Training","created":"2022-03-05","cited_by_count":29,"area":null},{"id":"2306.03824","title":"Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters","created":"2023-06-06","cited_by_count":27,"area":null},{"id":"2203.02073","title":"Differentially Private Label Protection in Split Learning","created":"2022-03-04","cited_by_count":26,"area":null},{"id":"2203.06735","title":"Private Non-Convex Federated Learning Without a Trusted Server","created":"2022-03-13","cited_by_count":26,"area":null},{"id":"2112.07246","title":"Federated Learning for Face Recognition with Gradient Correction","created":"2021-12-14","cited_by_count":24,"area":null},{"id":"2107.10663","title":"Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning","created":"2021-07-21","cited_by_count":23,"area":null},{"id":"2111.05326","title":"The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning","created":"2021-11-09","cited_by_count":23,"area":null},{"id":"2201.11986","title":"Gradient Masked Averaging for Federated Learning","created":"2022-01-28","cited_by_count":22,"area":null},{"id":"2108.02741","title":"GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning","created":"2021-08-05","cited_by_count":21,"area":null},{"id":"2102.06333","title":"Efficient Algorithms for Federated Saddle Point Optimization","created":"2021-02-12","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":"2106.05508","title":"Vertical Federated Learning without Revealing Intersection Membership","created":"2021-06-10","cited_by_count":19,"area":null},{"id":"2302.01463","title":"Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy","created":"2023-02-02","cited_by_count":18,"area":null},{"id":"2308.09883","title":"Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning","created":"2023-08-19","cited_by_count":17,"area":null},{"id":"2107.00778","title":"On Bridging Generic and Personalized Federated Learning for Image Classification","created":"2021-07-02","cited_by_count":15,"area":null},{"id":"2309.02250","title":"RoBoSS: A Robust, Bounded, Sparse, and Smooth Loss Function for Supervised Learning","created":"2023-09-05","cited_by_count":15,"area":null},{"id":"2206.05891","title":"Anchor Sampling for Federated Learning with Partial Client Participation","created":"2022-06-13","cited_by_count":14,"area":null},{"id":"2207.08204","title":"Fast Composite Optimization and Statistical Recovery in Federated Learning","created":"2022-07-17","cited_by_count":12,"area":null},{"id":"2210.13111","title":"Federated Learning and Meta Learning: Approaches, Applications, and Directions","created":"2022-10-24","cited_by_count":12,"area":null},{"id":"2405.11667","title":"The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication","created":"2024-05-19","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":"2203.15488","title":"Over-the-Air Federated Learning via Second-Order Optimization","created":"2022-03-29","cited_by_count":11,"area":null},{"id":"2107.10868","title":"Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time","created":"2021-07-22","cited_by_count":10,"area":null},{"id":"2208.07978","title":"Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion","created":"2022-08-16","cited_by_count":10,"area":null},{"id":"2109.03999","title":"Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies","created":"2021-09-09","cited_by_count":9,"area":null},{"id":"2206.07786","title":"Federated Data Analytics: A Study on Linear Models","created":"2022-06-15","cited_by_count":9,"area":null},{"id":"2509.10439","title":"Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration","created":"2025-09-12","cited_by_count":9,"area":null},{"id":"2111.14008","title":"Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling","created":"2021-11-28","cited_by_count":8,"area":null},{"id":"2301.09357","title":"Accelerating Fair Federated Learning: Adaptive Federated Adam","created":"2023-01-23","cited_by_count":8,"area":null},{"id":"2303.06530","title":"Making Batch Normalization Great in Federated Deep Learning","created":"2023-03-12","cited_by_count":8,"area":null},{"id":"2308.04077","title":"Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients","created":"2023-08-08","cited_by_count":8,"area":null},{"id":"2410.23131","title":"Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis","created":"2024-10-30","cited_by_count":8,"area":null},{"id":"2205.14905","title":"Confederated Learning: Federated Learning with Decentralized Edge Servers","created":"2022-05-30","cited_by_count":7,"area":null},{"id":"2208.12294","title":"DPAUC: Differentially Private AUC Computation in Federated Learning","created":"2022-08-25","cited_by_count":7,"area":null},{"id":"2211.01122","title":"Fast Adaptive Federated Bilevel Optimization","created":"2022-11-02","cited_by_count":7,"area":null},{"id":"2407.07084","title":"Stabilized Proximal-Point Methods for Federated Optimization","created":"2024-07-09","cited_by_count":7,"area":null},{"id":"2504.10770","title":"Collaborative Bayesian Optimization via Wasserstein Barycenters","created":"2025-04-15","cited_by_count":7,"area":null},{"id":"2110.02954","title":"A Stochastic Newton Algorithm for Distributed Convex Optimization","created":"2021-10-07","cited_by_count":6,"area":null},{"id":"2207.10751","title":"Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization","created":"2022-07-21","cited_by_count":6,"area":null},{"id":"2310.17761","title":"Distributed Personalized Empirical Risk Minimization","created":"2023-10-26","cited_by_count":6,"area":null},{"id":"2211.07303","title":"Adaptive Federated Minimax Optimization with Lower Complexities","created":"2022-11-14","cited_by_count":5,"area":null},{"id":"2405.11811","title":"FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning","created":"2024-05-20","cited_by_count":5,"area":null},{"id":"2206.01366","title":"Supernet Training for Federated Image Classification under System Heterogeneity","created":"2022-06-03","cited_by_count":4,"area":null},{"id":"2212.05602","title":"ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals","created":"2022-12-11","cited_by_count":4,"area":null},{"id":"2304.04169","title":"SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization","created":"2023-04-09","cited_by_count":4,"area":null},{"id":"2205.12412","title":"Differentially Private AUC Computation in Vertical Federated Learning","created":"2022-05-24","cited_by_count":3,"area":null},{"id":"2206.09576","title":"FedSSO: A Federated Server-Side Second-Order Optimization Algorithm","created":"2022-06-20","cited_by_count":3,"area":null},{"id":"2405.11079","title":"FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks","created":"2024-05-17","cited_by_count":3,"area":null},{"id":"2101.01505","title":"Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications","created":"2021-01-05","cited_by_count":2,"area":null},{"id":"2109.01326","title":"Statistical Estimation and Inference via Local SGD in Federated Learning","created":"2021-09-03","cited_by_count":2,"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":"2211.01883","title":"Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization","created":"2022-11-03","cited_by_count":2,"area":null},{"id":"2309.09719","title":"FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data","created":"2023-09-18","cited_by_count":2,"area":null},{"id":"2402.18018","title":"Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach","created":"2024-02-28","cited_by_count":2,"area":null},{"id":"2408.08642","title":"The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy","created":"2024-08-16","cited_by_count":2,"area":null},{"id":"2111.04550","title":"ARFED: Attack-Resistant Federated averaging based on outlier elimination","created":"2021-11-08","cited_by_count":1,"area":null},{"id":"2301.09500","title":"Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression","created":"2023-01-23","cited_by_count":1,"area":null},{"id":"2309.01150","title":"FedFwd: Federated Learning without Backpropagation","created":"2023-09-03","cited_by_count":1,"area":null},{"id":"2409.11509","title":"FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction","created":"2024-09-17","cited_by_count":1,"area":null},{"id":"2411.01548","title":"Analysis of regularized federated learning","created":"2024-11-03","cited_by_count":1,"area":null},{"id":"2503.04946","title":"Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation","created":"2025-03-06","cited_by_count":1,"area":null},{"id":"2511.06132","title":"On the Convergence and Stability of Distributed Sub-model Training","created":"2025-11-08","cited_by_count":1,"area":null},{"id":"2604.25467","title":"Subspace Optimization for Efficient Federated Learning under Heterogeneous Data","created":"2026-04-28","cited_by_count":1,"area":null},{"id":"2107.01707","title":"Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning","created":"2021-07-04","cited_by_count":0,"area":null},{"id":"2305.12979","title":"When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework","created":"2023-05-22","cited_by_count":0,"area":null},{"id":"2305.15643","title":"Federated Composite Saddle Point Optimization","created":"2023-05-25","cited_by_count":0,"area":null},{"id":"2501.14652","title":"Decoupled SGDA for Games with Intermittent Strategy Communication","created":"2025-01-24","cited_by_count":0,"area":null},{"id":"2505.23588","title":"Accelerated Training of Federated Learning via Second-Order Methods","created":"2025-05-29","cited_by_count":0,"area":null},{"id":"2601.12917","title":"CooperLLM: Cloud-Edge-End Cooperative Federated Fine-tuning for LLMs via ZOO-based Gradient Correction","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2606.01128","title":"Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2607.04254","title":"Air-Plan: Query-Optimized Topology Selection for Over-the-Air Decentralized Federated Learning","created":"2026-07-05","cited_by_count":0,"area":null}],"next_cursor":null}