{"total":672,"rows":[{"id":"2102.07078","title":"Exploiting Shared Representations for Personalized Federated Learning","created":"2021-02-14","cited_by_count":541,"area":null},{"id":"2103.00710","title":"Towards Personalized Federated Learning","created":"2021-03-01","cited_by_count":464,"area":null},{"id":"2106.06843","title":"Federated Learning on Non-IID Data: A Survey","created":"2021-06-12","cited_by_count":346,"area":null},{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"area":null},{"id":"2106.05001","title":"No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data","created":"2021-06-09","cited_by_count":241,"area":null},{"id":"2103.04628","title":"Personalized Federated Learning using Hypernetworks","created":"2021-03-08","cited_by_count":212,"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":"2108.10252","title":"Federated Multi-Task Learning under a Mixture of Distributions","created":"2021-08-23","cited_by_count":192,"area":null},{"id":"2106.06042","title":"FedBABU: Towards Enhanced Representation for Federated Image Classification","created":"2021-06-04","cited_by_count":182,"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":"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":"2108.00401","title":"Advances in adversarial attacks and defenses in computer vision: A survey","created":"2021-08-01","cited_by_count":127,"area":null},{"id":"2204.03809","title":"Federated Learning with Partial Model Personalization","created":"2022-04-08","cited_by_count":114,"area":null},{"id":"2111.02862","title":"Parameterized Knowledge Transfer for Personalized Federated Learning","created":"2021-11-04","cited_by_count":109,"area":null},{"id":"2107.00233","title":"FedMix: Approximation of Mixup under Mean Augmented Federated Learning","created":"2021-07-01","cited_by_count":108,"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":"2203.11834","title":"Improving Generalization in Federated Learning by Seeking Flat Minima","created":"2022-03-22","cited_by_count":98,"area":null},{"id":"2111.12444","title":"Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications","created":"2021-11-24","cited_by_count":97,"area":null},{"id":"2306.11867","title":"Personalized Federated Learning with Feature Alignment and Classifier Collaboration","created":"2023-06-20","cited_by_count":97,"area":null},{"id":"2106.08283","title":"CRFL: Certifiably Robust Federated Learning against Backdoor Attacks","created":"2021-06-15","cited_by_count":91,"area":null},{"id":"2309.15698","title":"Deep Model Fusion: A Survey","created":"2023-09-27","cited_by_count":91,"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":"2102.08503","title":"Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications","created":"2021-02-16","cited_by_count":85,"area":null},{"id":"2104.08815","title":"FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks","created":"2021-04-18","cited_by_count":85,"area":null},{"id":"2102.03448","title":"Federated Reconstruction: Partially Local Federated Learning","created":"2021-02-05","cited_by_count":79,"area":null},{"id":"2306.05980","title":"Federated Learning for Medical Image Analysis: A Survey","created":"2023-06-09","cited_by_count":78,"area":null},{"id":"2106.15482","title":"Personalized Federated Learning with Gaussian Processes","created":"2021-06-29","cited_by_count":76,"area":null},{"id":"2106.04159","title":"Fast Federated Learning in the Presence of Arbitrary Device Unavailability","created":"2021-06-08","cited_by_count":72,"area":null},{"id":"2102.02514","title":"FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning","created":"2021-02-04","cited_by_count":69,"area":null},{"id":"2106.05223","title":"Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling","created":"2021-06-09","cited_by_count":69,"area":null},{"id":"2206.07977","title":"Personalized Federated Learning via Variational Bayesian Inference","created":"2022-06-16","cited_by_count":68,"area":null},{"id":"2402.05859","title":"Learning to Route Among Specialized Experts for Zero-Shot Generalization","created":"2024-02-08","cited_by_count":65,"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":"2102.13101","title":"Federated Multi-armed Bandits with Personalization","created":"2021-02-25","cited_by_count":62,"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":"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":"2111.09360","title":"Personalized Federated Learning through Local Memorization","created":"2021-11-17","cited_by_count":57,"area":null},{"id":"2107.12048","title":"Decentralized Federated Learning: Balancing Communication and Computing Costs","created":"2021-07-26","cited_by_count":55,"area":null},{"id":"2110.08394","title":"Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning","created":"2021-10-15","cited_by_count":53,"area":null},{"id":"2204.05011","title":"FederatedScope: A Flexible Federated Learning Platform for Heterogeneity","created":"2022-04-11","cited_by_count":53,"area":null},{"id":"2103.10481","title":"Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations","created":"2021-03-18","cited_by_count":51,"area":null},{"id":"2305.06360","title":"Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy","created":"2023-05-10","cited_by_count":51,"area":"safety"},{"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":"2108.09749","title":"Flexible Clustered Federated Learning for Client-Level Data Distribution Shift","created":"2021-08-22","cited_by_count":47,"area":null},{"id":"2112.06053","title":"FedSoft: Soft Clustered Federated Learning with Proximal Local Updating","created":"2021-12-11","cited_by_count":46,"area":null},{"id":"2210.11653","title":"PaCo: Parameter-Compositional Multi-Task Reinforcement Learning","created":"2022-10-21","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":"2209.04599","title":"Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation","created":"2022-09-10","cited_by_count":45,"area":"distill"},{"id":"2104.14628","title":"Cluster-driven Graph Federated Learning over Multiple Domains","created":"2021-04-29","cited_by_count":44,"area":null},{"id":"2108.07792","title":"Federated Multi-Target Domain Adaptation","created":"2021-08-17","cited_by_count":44,"area":null},{"id":"2206.03655","title":"pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning","created":"2022-06-08","cited_by_count":44,"area":null},{"id":"2103.13822","title":"FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning","created":"2021-03-24","cited_by_count":43,"area":null},{"id":"2102.05218","title":"FLOP: Federated Learning on Medical Datasets using Partial Networks","created":"2021-02-10","cited_by_count":42,"area":null},{"id":"2210.04505","title":"A Survey on Heterogeneous Federated Learning","created":"2022-10-10","cited_by_count":42,"area":null},{"id":"2301.01299","title":"Recent Advances on Federated Learning: A Systematic Survey","created":"2023-01-03","cited_by_count":41,"area":null},{"id":"2107.10996","title":"Communication Efficiency in Federated Learning: Achievements and Challenges","created":"2021-07-23","cited_by_count":40,"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":"2205.10920","title":"Test-Time Robust Personalization for Federated Learning","created":"2022-05-22","cited_by_count":39,"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":"2305.02776","title":"Efficient Personalized Federated Learning via Sparse Model-Adaptation","created":"2023-05-04","cited_by_count":38,"area":null},{"id":"2103.00958","title":"Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating","created":"2021-03-01","cited_by_count":37,"area":null},{"id":"2305.01068","title":"Personalized Federated Learning under Mixture of Distributions","created":"2023-05-01","cited_by_count":37,"area":null},{"id":"2111.13394","title":"Non-IID data and Continual Learning processes in Federated Learning: A long road ahead","created":"2021-11-26","cited_by_count":36,"area":null},{"id":"2206.07902","title":"On Privacy and Personalization in Cross-Silo Federated Learning","created":"2022-06-16","cited_by_count":36,"area":null},{"id":"2209.10526","title":"Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces","created":"2022-09-21","cited_by_count":36,"area":null},{"id":"2305.20057","title":"Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance","created":"2023-05-31","cited_by_count":36,"area":null},{"id":"2101.03064","title":"One-Class Classification: A Survey","created":"2021-01-08","cited_by_count":35,"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":"2108.09103","title":"Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks","created":"2021-08-20","cited_by_count":35,"area":null},{"id":"2206.03611","title":"FedPop: A Bayesian Approach for Personalised Federated Learning","created":"2022-06-07","cited_by_count":35,"area":null},{"id":"2102.09743","title":"Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques","created":"2021-02-19","cited_by_count":34,"area":null},{"id":"2105.03170","title":"FedGL: Federated Graph Learning Framework with Global Self-Supervision","created":"2021-05-07","cited_by_count":34,"area":null},{"id":"2204.03880","title":"CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning","created":"2022-04-08","cited_by_count":34,"area":null},{"id":"2303.02213","title":"Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions","created":"2023-03-03","cited_by_count":33,"area":null},{"id":"2107.13892","title":"QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning","created":"2021-07-29","cited_by_count":32,"area":"distill"},{"id":"2109.04833","title":"Multimodal Federated Learning on IoT Data","created":"2021-09-10","cited_by_count":32,"area":null},{"id":"2110.14153","title":"Differentially Private Federated Bayesian Optimization with Distributed Exploration","created":"2021-10-27","cited_by_count":32,"area":null},{"id":"2103.03918","title":"FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data","created":"2021-03-05","cited_by_count":31,"area":null},{"id":"2110.01463","title":"Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits","created":"2021-10-04","cited_by_count":31,"area":null},{"id":"2110.10223","title":"A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison","created":"2021-10-19","cited_by_count":31,"area":null},{"id":"2201.02932","title":"A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning","created":"2022-01-09","cited_by_count":31,"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":"2106.02743","title":"SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks","created":"2021-06-04","cited_by_count":30,"area":null},{"id":"2202.07757","title":"Architecture Agnostic Federated Learning for Neural Networks","created":"2022-02-15","cited_by_count":30,"area":null},{"id":"2205.06730","title":"Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints","created":"2022-05-13","cited_by_count":30,"area":null},{"id":"2112.05928","title":"Efficient Device Scheduling with Multi-Job Federated Learning","created":"2021-12-11","cited_by_count":29,"area":null},{"id":"2502.07855","title":"Vision-Language Models for Edge Networks: A Comprehensive Survey","created":"2025-02-11","cited_by_count":29,"area":"mllm"},{"id":"2106.13973","title":"Benchmarking Differential Privacy and Federated Learning for BERT Models","created":"2021-06-26","cited_by_count":28,"area":null},{"id":"2102.07148","title":"A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization","created":"2021-02-14","cited_by_count":27,"area":null},{"id":"2105.00562","title":"Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity","created":"2021-05-02","cited_by_count":27,"area":null},{"id":"2210.07615","title":"FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning","created":"2022-10-14","cited_by_count":27,"area":null},{"id":"2212.03714","title":"Reconstructing Training Data from Model Gradient, Provably","created":"2022-12-07","cited_by_count":27,"area":null},{"id":"2404.02478","title":"FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning","created":"2024-04-03","cited_by_count":27,"area":null},{"id":"2102.03401","title":"Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles","created":"2021-02-05","cited_by_count":26,"area":null},{"id":"2105.12497","title":"Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI","created":"2021-05-26","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":"2112.00734","title":"Personalized Federated Learning with Adaptive Batchnorm for Healthcare","created":"2021-12-01","cited_by_count":26,"area":null}],"next_cursor":"100"}