{"total":143,"rows":[{"id":"2102.07623","title":"FedBN: Federated Learning on Non-IID Features via Local Batch Normalization","created":"2021-02-15","cited_by_count":571,"area":null},{"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":"2308.08165","title":"Stochastic Controlled Averaging for Federated Learning with Communication Compression","created":"2023-08-16","cited_by_count":101,"area":null},{"id":"2106.10874","title":"FedCM: Federated Learning with Client-level Momentum","created":"2021-06-21","cited_by_count":77,"area":null},{"id":"2106.07820","title":"On Large-Cohort Training for Federated Learning","created":"2021-06-15","cited_by_count":74,"area":null},{"id":"2210.08090","title":"Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning","created":"2022-10-14","cited_by_count":71,"area":null},{"id":"2106.13673","title":"Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy","created":"2021-06-25","cited_by_count":69,"area":null},{"id":"2207.14130","title":"FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning","created":"2022-07-28","cited_by_count":64,"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":"2201.09531","title":"Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning","created":"2022-01-24","cited_by_count":59,"area":null},{"id":"2302.10429","title":"FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy","created":"2023-02-21","cited_by_count":49,"area":null},{"id":"2305.11584","title":"Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape","created":"2023-05-19","cited_by_count":46,"area":null},{"id":"2204.10607","title":"Federated Learning via Inexact ADMM","created":"2022-04-22","cited_by_count":44,"area":null},{"id":"2106.10435","title":"STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning","created":"2021-06-19","cited_by_count":43,"area":null},{"id":"2108.09875","title":"Anarchic Federated Learning","created":"2021-08-23","cited_by_count":42,"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":"2306.16504","title":"Momentum Benefits Non-IID Federated Learning Simply and Provably","created":"2023-06-28","cited_by_count":38,"area":null},{"id":"2203.15104","title":"FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation","created":"2022-03-28","cited_by_count":36,"area":null},{"id":"2106.02305","title":"Local Adaptivity in Federated Learning: Convergence and Consistency","created":"2021-06-04","cited_by_count":31,"area":null},{"id":"2202.07757","title":"Architecture Agnostic Federated Learning for Neural Networks","created":"2022-02-15","cited_by_count":30,"area":null},{"id":"2103.03452","title":"FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization","created":"2021-03-05","cited_by_count":29,"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":"2404.18848","title":"FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition","created":"2024-04-29","cited_by_count":26,"area":null},{"id":"2204.03529","title":"FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity","created":"2022-04-07","cited_by_count":25,"area":null},{"id":"2507.20016","title":"FedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging","created":"2025-07-26","cited_by_count":25,"area":null},{"id":"2109.08119","title":"Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer","created":"2021-09-16","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":"2308.00522","title":"Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup","created":"2023-07-30","cited_by_count":23,"area":null},{"id":"2304.13267","title":"Bayesian Federated Learning: A Survey","created":"2023-04-26","cited_by_count":22,"area":null},{"id":"2204.13170","title":"AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation","created":"2022-04-27","cited_by_count":21,"area":null},{"id":"2306.05706","title":"Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization","created":"2023-06-09","cited_by_count":21,"area":null},{"id":"2208.03635","title":"Federated Adversarial Learning: A Framework with Convergence Analysis","created":"2022-08-07","cited_by_count":17,"area":null},{"id":"2205.12493","title":"Federated Self-supervised Learning for Heterogeneous Clients","created":"2022-05-25","cited_by_count":16,"area":null},{"id":"2304.05397","title":"Accelerating Hybrid Federated Learning Convergence under Partial Participation","created":"2023-04-10","cited_by_count":15,"area":null},{"id":"2306.02426","title":"Resilient Constrained Learning","created":"2023-06-04","cited_by_count":15,"area":null},{"id":"2211.00184","title":"FL Games: A Federated Learning Framework for Distribution Shifts","created":"2022-10-31","cited_by_count":14,"area":null},{"id":"2302.11051","title":"Fusion of Global and Local Knowledge for Personalized Federated Learning","created":"2023-02-21","cited_by_count":14,"area":null},{"id":"2108.06453","title":"Efficient Federated Meta-Learning over Multi-Access Wireless Networks","created":"2021-08-14","cited_by_count":13,"area":null},{"id":"2411.12377","title":"Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions","created":"2024-11-19","cited_by_count":13,"area":null},{"id":"2106.11264","title":"Compositional federated learning: Applications in distributionally robust averaging and meta learning","created":"2021-06-21","cited_by_count":12,"area":null},{"id":"2409.12626","title":"Green Federated Learning: A new era of Green Aware AI","created":"2024-09-19","cited_by_count":12,"area":null},{"id":"2104.00365","title":"Federated Few-Shot Learning with Adversarial Learning","created":"2021-04-01","cited_by_count":11,"area":null},{"id":"2209.02428","title":"Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning","created":"2022-09-02","cited_by_count":11,"area":null},{"id":"2310.10089","title":"Over-the-Air Federated Learning and Optimization","created":"2023-10-16","cited_by_count":10,"area":null},{"id":"2404.08447","title":"Federated Optimization with Doubly Regularized Drift Correction","created":"2024-04-12","cited_by_count":10,"area":null},{"id":"2112.15486","title":"Efficient and Reliable Overlay Networks for Decentralized Federated Learning","created":"2021-12-12","cited_by_count":9,"area":null},{"id":"2212.01519","title":"Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework","created":"2022-12-03","cited_by_count":9,"area":null},{"id":"2205.01438","title":"FedGiA: An Efficient Hybrid Algorithm for Federated Learning","created":"2022-05-03","cited_by_count":8,"area":null},{"id":"2112.15287","title":"Distributed Random Reshuffling over Networks","created":"2021-12-31","cited_by_count":7,"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":"2301.00955","title":"Differentially Private Federated Clustering over Non-IID Data","created":"2023-01-03","cited_by_count":7,"area":null},{"id":"2309.01795","title":"Composite federated learning with heterogeneous data","created":"2023-09-04","cited_by_count":7,"area":null},{"id":"2409.18448","title":"Hierarchical Federated Learning with Multi-Timescale Gradient Correction","created":"2024-09-27","cited_by_count":7,"area":null},{"id":"2308.08290","title":"DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning","created":"2023-08-16","cited_by_count":6,"area":null},{"id":"2402.09941","title":"FedLion: Faster Adaptive Federated Optimization with Fewer Communication","created":"2024-02-15","cited_by_count":6,"area":null},{"id":"2503.02154","title":"AugFL: Augmenting Federated Learning with Pretrained Models","created":"2025-03-04","cited_by_count":6,"area":null},{"id":"2204.12284","title":"Federated Stochastic Primal-dual Learning with Differential Privacy","created":"2022-04-26","cited_by_count":5,"area":null},{"id":"2312.15148","title":"Personalized Federated Learning with Attention-based Client Selection","created":"2023-12-23","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":"2211.04218","title":"Clustered Federated Learning based on Nonconvex Pairwise Fusion","created":"2022-11-08","cited_by_count":4,"area":null},{"id":"2301.12677","title":"Distributed Stochastic Optimization under a General Variance Condition","created":"2023-01-30","cited_by_count":4,"area":null},{"id":"2310.10117","title":"Federated Learning with Convex Global and Local Constraints","created":"2023-10-16","cited_by_count":4,"area":null},{"id":"2401.17460","title":"Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method","created":"2024-01-30","cited_by_count":4,"area":null},{"id":"2402.13989","title":"FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning","created":"2024-02-21","cited_by_count":4,"area":null},{"id":"2405.10513","title":"Federated Learning With Energy Harvesting Devices: An MDP Framework","created":"2024-05-17","cited_by_count":4,"area":null},{"id":"2501.13516","title":"Communication-Efficient Stochastic Distributed Learning","created":"2025-01-23","cited_by_count":4,"area":null},{"id":"2501.17325","title":"Connecting Federated ADMM to Bayes","created":"2025-01-28","cited_by_count":4,"area":null},{"id":"2504.11903","title":"FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data","created":"2025-04-16","cited_by_count":4,"area":null},{"id":"2509.05213","title":"An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data","created":"2025-09-05","cited_by_count":4,"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":"2303.06314","title":"Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout","created":"2023-03-11","cited_by_count":3,"area":null},{"id":"2310.03461","title":"Towards Understanding Generalization and Stability Gaps between Centralized and Decentralized Federated Learning","created":"2023-10-05","cited_by_count":3,"area":null},{"id":"2310.09858","title":"Federated Reinforcement Learning for Resource Allocation in V2X Networks","created":"2023-10-15","cited_by_count":3,"area":null},{"id":"2403.17572","title":"Enhancing Privacy in Federated Learning through Local Training","created":"2024-03-26","cited_by_count":3,"area":null},{"id":"2405.10618","title":"Distributed Event-Based Learning via ADMM","created":"2024-05-17","cited_by_count":3,"area":null},{"id":"2406.14420","title":"Communication-efficient Vertical Federated Learning via Compressed Error Feedback","created":"2024-06-20","cited_by_count":3,"area":null},{"id":"2506.13150","title":"Federated ADMM from Bayesian Duality","created":"2025-06-16","cited_by_count":3,"area":null},{"id":"2509.02970","title":"Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation","created":"2025-09-03","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":"2202.02502","title":"A Coalition Formation Game Approach for Personalized Federated Learning","created":"2022-02-05","cited_by_count":2,"area":null},{"id":"2204.12663","title":"Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective","created":"2022-04-27","cited_by_count":2,"area":null},{"id":"2206.10815","title":"FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus","created":"2022-06-22","cited_by_count":2,"area":null},{"id":"2209.10537","title":"FedFOR: Stateless Heterogeneous Federated Learning with First-Order Regularization","created":"2022-09-21","cited_by_count":2,"area":null},{"id":"2303.07067","title":"Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection","created":"2023-03-13","cited_by_count":2,"area":null},{"id":"2305.10294","title":"DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime","created":"2023-05-17","cited_by_count":2,"area":null},{"id":"2306.07959","title":"Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings","created":"2023-06-13","cited_by_count":2,"area":null},{"id":"2306.12037","title":"Distributed Random Reshuffling Methods with Improved Convergence","created":"2023-06-21","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":"2310.19558","title":"Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification","created":"2023-10-30","cited_by_count":2,"area":null},{"id":"2402.17705","title":"Federated Learning for Estimating Heterogeneous Treatment Effects","created":"2024-02-27","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":"2403.02347","title":"On the Convergence of Federated Learning Algorithms without Data Similarity","created":"2024-02-29","cited_by_count":2,"area":null},{"id":"2409.18915","title":"A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs","created":"2024-09-27","cited_by_count":2,"area":null},{"id":"2410.07272","title":"Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration","created":"2024-10-09","cited_by_count":2,"area":null},{"id":"2501.11267","title":"Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks","created":"2025-01-20","cited_by_count":2,"area":null},{"id":"2502.10784","title":"Preconditioned Inexact Stochastic ADMM for Deep Model","created":"2025-02-15","cited_by_count":2,"area":null},{"id":"2508.12576","title":"Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg","created":"2025-08-18","cited_by_count":2,"area":null},{"id":"2206.15387","title":"Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning","created":"2022-06-30","cited_by_count":1,"area":null},{"id":"2208.11231","title":"Exact Penalty Method for Federated Learning","created":"2022-08-23","cited_by_count":1,"area":null}],"next_cursor":"100"}