{"total":379,"rows":[{"id":"2103.00710","title":"Towards Personalized Federated Learning","created":"2021-03-01","cited_by_count":464,"area":null},{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"area":null},{"id":"2102.13451","title":"FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout","created":"2021-02-26","cited_by_count":192,"area":null},{"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":"2206.02618","title":"Generalized Federated Learning via Sharpness Aware Minimization","created":"2022-06-06","cited_by_count":144,"area":null},{"id":"2209.10083","title":"Federated Learning from Pre-Trained Models: A Contrastive Learning Approach","created":"2022-09-21","cited_by_count":142,"area":null},{"id":"2104.02151","title":"Distributed Learning in Wireless Networks: Recent Progress and Future Challenges","created":"2021-04-05","cited_by_count":129,"area":null},{"id":"2402.06954","title":"OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning","created":"2024-02-10","cited_by_count":128,"area":null},{"id":"2110.00857","title":"FairFed: Enabling Group Fairness in Federated Learning","created":"2021-10-02","cited_by_count":118,"area":null},{"id":"2111.02862","title":"Parameterized Knowledge Transfer for Personalized Federated Learning","created":"2021-11-04","cited_by_count":109,"area":null},{"id":"2102.12660","title":"Distributionally Robust Federated Averaging","created":"2021-02-25","cited_by_count":104,"area":null},{"id":"2311.06750","title":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark","created":"2023-11-12","cited_by_count":99,"area":null},{"id":"2103.00697","title":"Heterogeneity for the Win: One-Shot Federated Clustering","created":"2021-03-01","cited_by_count":87,"area":null},{"id":"2306.01603","title":"Decentralized Federated Learning: A Survey and Perspective","created":"2023-06-02","cited_by_count":86,"area":null},{"id":"2106.07820","title":"On Large-Cohort Training for Federated Learning","created":"2021-06-15","cited_by_count":74,"area":null},{"id":"2305.19229","title":"FedDisco: Federated Learning with Discrepancy-Aware Collaboration","created":"2023-05-30","cited_by_count":67,"area":null},{"id":"2104.14362","title":"From Distributed Machine Learning to Federated Learning: A Survey","created":"2021-04-29","cited_by_count":66,"area":null},{"id":"2104.13417","title":"Towards Fair Federated Learning with Zero-Shot Data Augmentation","created":"2021-04-27","cited_by_count":64,"area":null},{"id":"2102.01854","title":"Provably Secure Federated Learning against Malicious Clients","created":"2021-02-03","cited_by_count":63,"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":"2211.01549","title":"Client Selection in Federated Learning: Principles, Challenges, and Opportunities","created":"2022-11-03","cited_by_count":62,"area":null},{"id":"2104.14937","title":"Federated Learning with Fair Averaging","created":"2021-04-30","cited_by_count":61,"area":null},{"id":"2101.12204","title":"Federated Multi-Armed Bandits","created":"2021-01-28","cited_by_count":59,"area":null},{"id":"2106.04502","title":"Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing","created":"2021-06-08","cited_by_count":58,"area":null},{"id":"2111.01872","title":"Towards Fairness-Aware Federated Learning","created":"2021-11-02","cited_by_count":58,"area":null},{"id":"2107.11621","title":"FedLab: A Flexible Federated Learning Framework","created":"2021-07-24","cited_by_count":55,"area":null},{"id":"2205.02719","title":"Communication-Efficient Adaptive Federated Learning","created":"2022-05-05","cited_by_count":55,"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":"2303.16520","title":"Fair Federated Medical Image Segmentation via Client Contribution Estimation","created":"2023-03-29","cited_by_count":52,"area":null},{"id":"2108.08435","title":"Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning","created":"2021-08-19","cited_by_count":50,"area":null},{"id":"2110.11027","title":"FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion","created":"2021-10-21","cited_by_count":49,"area":null},{"id":"2201.01601","title":"FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients","created":"2022-01-05","cited_by_count":46,"area":null},{"id":"2205.11506","title":"Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering","created":"2022-05-23","cited_by_count":45,"area":null},{"id":"2108.02943","title":"Unsupervised Learning of Debiased Representations with Pseudo-Attributes","created":"2021-08-06","cited_by_count":44,"area":null},{"id":"2109.06141","title":"On Tilted Losses in Machine Learning: Theory and Applications","created":"2021-09-13","cited_by_count":43,"area":null},{"id":"2108.09875","title":"Anarchic Federated Learning","created":"2021-08-23","cited_by_count":42,"area":null},{"id":"2203.10144","title":"Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation","created":"2022-03-18","cited_by_count":42,"area":null},{"id":"2101.00787","title":"Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation","created":"2021-01-04","cited_by_count":40,"area":null},{"id":"2106.09580","title":"Optimality and Stability in Federated Learning: A Game-theoretic Approach","created":"2021-06-17","cited_by_count":40,"area":null},{"id":"2103.03228","title":"One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning","created":"2021-03-04","cited_by_count":39,"area":null},{"id":"2107.08147","title":"AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning","created":"2021-07-16","cited_by_count":39,"area":null},{"id":"2110.15545","title":"Improving Fairness via Federated Learning","created":"2021-10-29","cited_by_count":39,"area":null},{"id":"2205.10920","title":"Test-Time Robust Personalization for Federated Learning","created":"2022-05-22","cited_by_count":39,"area":null},{"id":"2109.08604","title":"Enforcing fairness in private federated learning via the modified method of differential multipliers","created":"2021-09-17","cited_by_count":38,"area":null},{"id":"2102.00875","title":"Scaling Federated Learning for Fine-tuning of Large Language Models","created":"2021-02-01","cited_by_count":36,"area":null},{"id":"2102.12920","title":"Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning","created":"2021-02-25","cited_by_count":35,"area":null},{"id":"2402.15638","title":"Fair Resource Allocation in Multi-Task Learning","created":"2024-02-23","cited_by_count":35,"area":null},{"id":"2108.02117","title":"FedJAX: Federated learning simulation with JAX","created":"2021-08-04","cited_by_count":32,"area":null},{"id":"2111.11066","title":"FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks","created":"2021-11-22","cited_by_count":32,"area":null},{"id":"2210.10837","title":"On Learning Fairness and Accuracy on Multiple Subgroups","created":"2022-10-19","cited_by_count":32,"area":null},{"id":"2206.09262","title":"Motley: Benchmarking Heterogeneity and Personalization in Federated Learning","created":"2022-06-18","cited_by_count":31,"area":null},{"id":"2307.07393","title":"L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning","created":"2023-07-14","cited_by_count":30,"area":null},{"id":"2405.10853","title":"The Future of Large Language Model Pre-training is Federated","created":"2024-05-17","cited_by_count":30,"area":null},{"id":"2405.16585","title":"Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity","created":"2024-05-26","cited_by_count":30,"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":"2110.00135","title":"UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis","created":"2021-10-01","cited_by_count":28,"area":null},{"id":"2109.05662","title":"Training Fair Models in Federated Learning without Data Privacy Infringement","created":"2021-09-13","cited_by_count":26,"area":null},{"id":"2111.01108","title":"Resource-Efficient Federated Learning","created":"2021-11-01","cited_by_count":26,"area":null},{"id":"2207.06343","title":"TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels","created":"2022-07-13","cited_by_count":26,"area":null},{"id":"2306.14123","title":"Privacy and Fairness in Federated Learning: on the Perspective of Trade-off","created":"2023-06-25","cited_by_count":26,"area":null},{"id":"2201.00072","title":"BARACK: Partially Supervised Group Robustness With Guarantees","created":"2021-12-31","cited_by_count":25,"area":null},{"id":"2201.08304","title":"Minimax Demographic Group Fairness in Federated Learning","created":"2022-01-20","cited_by_count":24,"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":"2112.13246","title":"Towards Federated Learning on Time-Evolving Heterogeneous Data","created":"2021-12-25","cited_by_count":23,"area":null},{"id":"2201.09046","title":"Differentially Private SGDA for Minimax Problems","created":"2022-01-22","cited_by_count":23,"area":null},{"id":"2211.02091","title":"Fairness in Federated Learning via Core-Stability","created":"2022-11-03","cited_by_count":23,"area":null},{"id":"2310.18816","title":"Adaptive Test-Time Personalization for Federated Learning","created":"2023-10-28","cited_by_count":23,"area":null},{"id":"2109.09046","title":"Improving Fairness for Data Valuation in Horizontal Federated Learning","created":"2021-09-19","cited_by_count":22,"area":null},{"id":"2103.15947","title":"Federated Learning with Taskonomy for Non-IID Data","created":"2021-03-29","cited_by_count":21,"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":"2202.01666","title":"Proportional Fairness in Federated Learning","created":"2022-02-03","cited_by_count":21,"area":null},{"id":"2105.07603","title":"EasyFL: A Low-code Federated Learning Platform For Dummies","created":"2021-05-17","cited_by_count":20,"area":null},{"id":"2204.12493","title":"One-shot Federated Learning without Server-side Training","created":"2022-04-26","cited_by_count":20,"area":null},{"id":"2205.01608","title":"Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction","created":"2022-05-03","cited_by_count":20,"area":null},{"id":"2206.10546","title":"FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity","created":"2022-06-21","cited_by_count":20,"area":null},{"id":"2205.13797","title":"AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation","created":"2022-05-27","cited_by_count":19,"area":null},{"id":"2304.07421","title":"Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition","created":"2023-04-14","cited_by_count":19,"area":null},{"id":"2306.06508","title":"Optimizing the Collaboration Structure in Cross-Silo Federated Learning","created":"2023-06-10","cited_by_count":19,"area":null},{"id":"2102.01375","title":"Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges","created":"2021-02-02","cited_by_count":17,"area":null},{"id":"2104.02988","title":"Optimal Algorithms for Differentially Private Stochastic Monotone Variational Inequalities and Saddle-Point Problems","created":"2021-04-07","cited_by_count":17,"area":null},{"id":"2305.11386","title":"Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods","created":"2023-05-19","cited_by_count":17,"area":null},{"id":"2104.07515","title":"FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems","created":"2021-04-15","cited_by_count":16,"area":null},{"id":"2108.07926","title":"Collaboration Equilibrium in Federated Learning","created":"2021-08-18","cited_by_count":16,"area":null},{"id":"2203.06338","title":"Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation","created":"2022-03-12","cited_by_count":16,"area":null},{"id":"2204.08069","title":"Self-Aware Personalized Federated Learning","created":"2022-04-17","cited_by_count":16,"area":null},{"id":"2212.07224","title":"FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation","created":"2022-12-14","cited_by_count":16,"area":null},{"id":"2308.11841","title":"A Survey for Federated Learning Evaluations: Goals and Measures","created":"2023-08-23","cited_by_count":16,"area":null},{"id":"2109.00675","title":"FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning","created":"2021-09-02","cited_by_count":15,"area":null},{"id":"2111.04986","title":"Unified Group Fairness on Federated Learning","created":"2021-11-09","cited_by_count":15,"area":null},{"id":"2109.00151","title":"Asynchronous Federated Learning for Sensor Data with Concept Drift","created":"2021-09-01","cited_by_count":14,"area":null},{"id":"2207.09611","title":"Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions","created":"2022-07-20","cited_by_count":14,"area":null},{"id":"2209.08005","title":"Stability and Generalization for Markov Chain Stochastic Gradient Methods","created":"2022-09-16","cited_by_count":14,"area":null},{"id":"2303.11673","title":"A Survey on Class Imbalance in Federated Learning","created":"2023-03-21","cited_by_count":13,"area":null},{"id":"2401.04993","title":"AdaFed: Fair Federated Learning via Adaptive Common Descent Direction","created":"2024-01-10","cited_by_count":13,"area":null},{"id":"2402.05968","title":"Federated Learning Priorities Under the European Union Artificial Intelligence Act","created":"2024-02-05","cited_by_count":13,"area":null},{"id":"2101.05880","title":"Auto-weighted Robust Federated Learning with Corrupted Data Sources","created":"2021-01-14","cited_by_count":12,"area":null},{"id":"2103.14051","title":"Tilted Cross Entropy (TCE): Promoting Fairness in Semantic Segmentation","created":"2021-03-25","cited_by_count":12,"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":"2110.14205","title":"FedPrune: Towards Inclusive Federated Learning","created":"2021-10-27","cited_by_count":12,"area":null},{"id":"2203.10190","title":"Fair Federated Learning via Bounded Group Loss","created":"2022-03-18","cited_by_count":12,"area":null}],"next_cursor":"100"}