{"total":29,"rows":[{"id":"2306.09486","title":"FedMultimodal: A Benchmark For Multimodal Federated Learning","created":"2023-06-15","cited_by_count":51,"area":null},{"id":"2209.13080","title":"FedStack: Personalized activity monitoring using stacked federated learning","created":"2022-09-27","cited_by_count":15,"area":null},{"id":"2407.06754","title":"Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges","created":"2024-07-09","cited_by_count":15,"area":null},{"id":"2502.07059","title":"Federated Continual Learning: Concepts, Challenges, and Solutions","created":"2025-02-10","cited_by_count":13,"area":null},{"id":"2209.11750","title":"Transformer-based Models to Deal with Heterogeneous Environments in Human Activity Recognition","created":"2022-09-22","cited_by_count":12,"area":null},{"id":"2207.08181","title":"Federated Continual Learning through distillation in pervasive computing","created":"2022-07-17","cited_by_count":10,"area":null},{"id":"2303.03908","title":"Client-specific Property Inference against Secure Aggregation in Federated Learning","created":"2023-03-07","cited_by_count":9,"area":null},{"id":"2305.08070","title":"A Survey of Federated Evaluation in Federated Learning","created":"2023-05-14","cited_by_count":7,"area":null},{"id":"2410.15681","title":"Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification","created":"2024-10-21","cited_by_count":7,"area":null},{"id":"2210.16918","title":"Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones","created":"2022-10-30","cited_by_count":6,"area":null},{"id":"2508.03766","title":"LLM-Prior: A Framework for Knowledge-Driven Prior Elicitation and Aggregation","created":"2025-08-05","cited_by_count":6,"area":null},{"id":"2506.00499","title":"Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case study","created":"2025-05-31","cited_by_count":5,"area":null},{"id":"2205.10864","title":"Federated Learning Aggregation: New Robust Algorithms with Guarantees","created":"2022-05-22","cited_by_count":4,"area":null},{"id":"2303.06246","title":"Zone-based Federated Learning for Mobile Sensing Data","created":"2023-03-10","cited_by_count":4,"area":null},{"id":"2312.11220","title":"A review of federated learning in renewable energy applications: Potential, challenges, and future directions","created":"2023-12-18","cited_by_count":3,"area":null},{"id":"2207.08180","title":"Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain","created":"2022-07-17","cited_by_count":2,"area":null},{"id":"2305.09856","title":"Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients","created":"2023-05-16","cited_by_count":2,"area":null},{"id":"2311.06917","title":"FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning","created":"2023-11-12","cited_by_count":2,"area":null},{"id":"2207.08187","title":"Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR","created":"2022-07-17","cited_by_count":1,"area":null},{"id":"2311.01201","title":"Federated Learning on Edge Sensing Devices: A Review","created":"2023-11-02","cited_by_count":1,"area":null},{"id":"2205.14236","title":"FedControl: When Control Theory Meets Federated Learning","created":"2022-05-27","cited_by_count":0,"area":null},{"id":"2307.06283","title":"Tackling Computational Heterogeneity in FL: A Few Theoretical Insights","created":"2023-07-12","cited_by_count":0,"area":null},{"id":"2308.02454","title":"SoK: Assessing the State of Applied Federated Machine Learning","created":"2023-08-03","cited_by_count":0,"area":null},{"id":"2311.15382","title":"Evaluating Multi-Global Server Architecture for Federated Learning","created":"2023-11-26","cited_by_count":0,"area":null},{"id":"2404.02779","title":"Federated Computing -- Survey on Building Blocks, Extensions and Systems","created":"2024-04-03","cited_by_count":0,"area":null},{"id":"2407.12287","title":"CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering","created":"2024-07-17","cited_by_count":0,"area":null},{"id":"2505.05486","title":"FedAvgen: Metadata for Model Aggregation In Communication Systems","created":"2025-04-28","cited_by_count":0,"area":null},{"id":"2509.13987","title":"Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response","created":"2025-09-17","cited_by_count":0,"area":null},{"id":"2607.03334","title":"FedAvg for HAR: Exploring the Tradeoff Between Personalized and Generalization Accuracy","created":"2026-07-03","cited_by_count":0,"area":null}],"next_cursor":null}