mlsanityData to 7 Oct 2026

AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

arXiv 2301.03062 ↗First posted 8 Jan 2023cs.LG cs.DC cs.SYcited by 10

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Abstract (arXiv)

In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 28 of its 51 references that are in the map. First posted on arXiv 8 Jan 2023.

Builds on

28 of 51 references.

References · 28

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2021Convex optimizationcited by 2,479
  5. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  7. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  8. 2020Federated Learning with Matched Averagingcited by 564
  9. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  10. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  11. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  12. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
16 more
  1. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  2. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  3. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  4. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  5. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
  6. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  7. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  8. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  9. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 80
  10. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  11. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 45
  12. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  13. 2021Optimal Rate Adaption in Federated Learning with Compressed Communicationscited by 15
  14. 2021Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networkscited by 14
  15. 2021Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networkscited by 10
  16. 2021FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computingcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 3; 2026: 2.

10 papers.

  1. 2023Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devicescited by 13
  2. 2023Federated Learning While Providing Model as a Service: Joint Training and Inference Optimizationcited by 8
  3. 2023Federated learning compression designed for lightweight communicationscited by 2
  4. 2025Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learningcited by 2
  5. 2025An Information-Theoretic Analysis for Federated Learning under Concept Driftcited by 2
  6. 2024FedGreen: Carbon-aware Federated Learning with Model Size Adaptationcited by 1
  7. 2024Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learningcited by 0
  8. 2025Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Schedulingcited by 0
  9. 2026Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communicationcited by 0
  10. 2026A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devicescited by 0