mlsanityData to 7 Oct 2026

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data

arXiv 2403.16460 ↗First posted 25 Mar 2024cs.LG cs.AI cs.DCcited by 25

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Abstract (arXiv)

Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity in federated learning (FL) by grouping similar clients for cluster-wise model training. However, current CFL methods struggle due to inadequate integration of global and intra-cluster knowledge and the absence of an efficient online model similarity metric, while treating the cluster count as a fixed hyperparameter limits flexibility and robustness. In this paper, we propose an adaptive CFL framework, named FedAC, which (1) efficiently integrates global knowledge into intra-cluster learning by decoupling neural networks and utilizing distinct aggregation methods for each submodule, significantly enhancing performance; (2) includes a costeffective online model similarity metric based on dimensionality reduction; (3) incorporates a cluster number fine-tuning module for improved adaptability and scalability in complex, heterogeneous environments. Extensive experiments show that FedAC achieves superior empirical performance, increasing the test accuracy by around 1.82% and 12.67% on CIFAR-10 and CIFAR-100 datasets, respectively, under different non-IID settings compared to SOTA methods.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 25 Mar 2024.

Builds on

23 of 38 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  5. 2019Federated Learning with Personalization Layerscited by 533
  6. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  7. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  8. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  9. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  10. 2021Personalized Federated Learning using Hypernetworkscited by 210
  11. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  12. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
11 more
  1. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  2. 2023Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunitiescited by 89
  3. 2023Split Learning in 6G Edge Networkscited by 68
  4. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  5. 2015Aggregating Deep Convolutional Features for Image Retrievalcited by 63
  6. 2020FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measurecited by 45
  7. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  8. 2023AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Drivingcited by 32
  9. 2024Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunitiescited by 32
  10. 2021Personalized Federated Learning with Contextualized Generalizationcited by 23
  11. 2023Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Drivingcited by 19

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Citing papers per year: 2024: 10; 2025: 11; 2026: 4.

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  1. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  2. 2024SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Modelscited by 60
  3. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  4. 2024SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Frameworkcited by 21
  5. 2024IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengerscited by 20
  6. 2024SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensingcited by 14
  7. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  8. 2025Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoringcited by 10
  9. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 7
  10. 2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attackscited by 7
  11. 2025LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learningcited by 6
  12. 2026Conflict-Aware Client Selection for Multi-Server Federated Learningcited by 5
13 more
  1. 2026NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learningcited by 4
  2. 2024Federated Learning Clients Clustering with Adaptation to Data Driftscited by 3
  3. 2025A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observationcited by 2
  4. 2025Sample Efficient Experience Replay in Non-stationary Environmentscited by 2
  5. 2024t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Drivingcited by 1
  6. 2025Secure Resource Allocation via Constrained Deep Reinforcement Learningcited by 1
  7. 2025Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computingcited by 1
  8. 2024Tracing Human Stress from Physiological Signals using UWB Radarcited by 0
  9. 2025DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protectioncited by 0
  10. 2025Taming Latency and Bandwidth: A Theoretical Framework and Adaptive Algorithm for Communication-Constrained Trainingcited by 0
  11. 2025FedOC: Multi-Server FL with Overlapping Client Relays in Wireless Edge Networkscited by 0
  12. 2026FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environmentscited by 0
  13. 2026CA-AFP: Cluster-Aware Adaptive Federated Pruningcited by 0