Key papers

• Federated Adversarial Domain Adaptation (2019)
  Xingchao Peng et al.
  10.99
• On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach (2026)
  Jiahui Bai et al.
  6.66
• LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data (2025)
  Yuxin Zhang et al.
  5.79
• Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training? (2021)
  Shuxiao Chen et al.
  5.72
• Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks (2025)
  Ebtisaam Alharbi et al.
  5.18
• FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning (2025)
  Yanbing Zhou et al.
  5.13
• Federated Learning with Sample-level Client Drift Mitigation (2025)
  Haoran Xu et al.
  5.13
• Generative AI and Federated Learning for Intrusion Detection Systems: A Survey (2026)
  Jiefei Liu et al.
  5.01
• Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets (2026)
  Rodrigo Tertulino et al.
  5.01
• Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning (2025)
  Ratun Rahman et al.
  4.76
• Federated Large Language Models: Current Progress and Future Directions (2024)
  Yuhang Yao et al.
  4.54
• TallyTrain: Communication-Efficient Federated Distillation (2026)
  Radhakrishna Achanta et al.
  4.39
• Federated Sovereign Transport Protocol (FSTP): Verifiable Coordination Without Disclosure (2026)
  Ram\'on Soto C. (Department of Accounting et al.
  4.39
• Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning (2026)
  Krishna Harsha Kovelakuntla Huthasana et al.
  4.39
• Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification (2026)
  Aizierjiang Aiersilan
  4.39
• Auto-FL-Research: Agentic Search for Federated Learning Algorithms (2026)
  Holger R. Roth et al.
  4.39
• Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning (2026)
  Haemin Park et al.
  4.39
• QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition (2026)
  Quoc Bao Phan et al.
  4.39
• Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data (2026)
  Xuanyu Chen et al.
  4.39
• Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework (2026)
  Xunkai Li et al.
  4.39
• Federated Lightweight Fine-Tuning (2026)
  Radhakrishna Achanta et al.
  4.39
• SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learning (2026)
  Jaeik Kim et al.
  4.39
• SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework (2026)
  Akarsh K Nair et al.
  4.39
• Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction (2026)
  Ruth Amey et al.
  4.39
• Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference (2026)
  Yehuda Rudin et al.
  4.39
• A Drift Stable Quantum Federated Learning for Intelligent Services (2026)
  Shanika Iroshi Nanayakkara et al.
  4.39
• Industrial Tokenization for LLM-Based Health Intelligence: A Federated Architecture for Industrial Evidence Integration (2026)
  Deshui Li et al.
  4.39
• QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning (2026)
  Nazmus Shakib Shadin et al.
  4.33
• FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching (2026)
  Haoran Zhang et al.
  4.33
• Compositional Generative Modeling from Decentralized Data (2026)
  Mashrur M. Morshed and Vishnu Naresh Boddeti
  4.33
• Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning (2026)
  Haengbok Chung and Jae Sung Lee
  4.33
• MoE Enhanced Federated Learning for Spatiotemporal Prediction (2026)
  Zhehao Dai et al.
  4.33
• A Survey on Federated Causal Discovery and Inference (2026)
  Xianjie Guo et al.
  4.33
• Federated Survival Analysis in Healthcare: A Multi-Model Evaluation on Cross-Institutional Heterogeneous Breast Cancer Data (2026)
  Natalia Moreno-Blasco et al.
  4.33
• Semi-asynchronous Federated Learning in Flower: Framework Extension and Performance Assessment (2026)
  V\'ictor Hidalgo-Izquierdo et al.
  4.33
• Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach (2026)
  Lianshuai Guo et al.
  4.33
• FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning (2026)
  Rachid Hedjam
  4.27
• FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data (2026)
  Maryam Moradpour et al.
  4.27
• Separate Aggregation of Split Network for Personalized Federated Learning (2026)
  Yunseok Kang et al.
  4.27
• Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences (2026)
  Jabin Koo et al.
  4.27
• Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation (2025)
  Xiaoxiao He et al.
  4.19
• SelfFed: Self-Supervised Federated Learning for Data Heterogeneity and Label Scarcity in Medical Images (2023)
  Sunder Ali Khowaja et al.
  4.18
• Mist-Assisted Federated Learning for Intrusion Detection in Heterogeneous IoT Networks (2025)
  Saadat Izadi et al.
  4.09
• The Role of Federated Learning in Improving Financial Security: A Survey (2025)
  Cade Houston Kennedy et al.
  4.03
• Floe: Federated Specialization for Real-Time LLM-SLM Inference (2026)
  Chunlin Tian et al.
  3.98
• Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare (2025)
  Natallia Kokash and Lei Wang and Thomas H. Gillespie and Adam Belloum and Paola Grosso and Sara Quinney and Lang Li and Bernard de Bono
  3.75
• Enhancing Efficiency in Multidevice Federated Learning through Data Selection (2022)
  Fan Mo et al.
  3.71
• Trusted Compute Units: A Framework for Chained Verifiable Computations (2025)
  Fernando Castillo et al.
  3.70
• Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation (2025)
  Xinge Ma et al.
  3.64
• Distributed Learning and Inference Systems: A Networking Perspective (2025)
  Hesham G. Moussa et al.
  3.53
• Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning (2025)
  Mingyuan Fan et al.
  3.53
• Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization (2024)
  Dun Zeng et al.
  3.47
• From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning (2026)
  Huong Nguyen et al.
  3.45
• Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models (2024)
  Sajjad Ghiasvand et al.
  3.42
• FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection (2026)
  Yutong He et al.
  3.34
• scBatchProx: Federated-Inspired Refinement for Stable Cell-Type Discriminability under Heterogeneous Batch Compositions (2026)
  Quang-Huy Nguyen and Jiaqi Wang and Wei-Shinn Ku
  3.23
• Achieving Linear Speedup for Composite Federated Learning (2026)
  Kun Huang et al.
  3.23
• FedPOD: the deployable units of training for federated learning (2025)
  Daewoon Kim et al.
  3.21
• Federated Learning Clients Clustering with Adaptation to Data Drifts (2024)
  Minghao Li (1) et al.
  3.20
• Semi-Supervised Federated Multi-Label Feature Selection with Fuzzy Information Measures (2025)
  Afsaneh Mahanipour et al.
  3.15