mlsanityData to 7 Oct 2026

FLAME: Federated Learning Across Multi-device Environments

arXiv 2202.08922 ↗First posted 17 Feb 2022cs.LGcited by 8

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Abstract (arXiv)

Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. While we witness increasing applications of FL in the area of mobile sensing, such as human activity recognition (HAR), FL has not been studied in the context of a multi-device environment (MDE), wherein each user owns multiple data-producing devices. With the proliferation of mobile and wearable devices, MDEs are increasingly becoming popular in ubicomp settings, therefore necessitating the study of FL in them. FL in MDEs is characterized by being not independent and identically distributed (non-IID) across clients, complicated by the presence of both user and device heterogeneities. Further, ensuring efficient utilization of system resources on FL clients in a MDE remains an important challenge. In this paper, we propose FLAME, a user-centered FL training approach to counter statistical and system heterogeneity in MDEs, and bring consistency in inference performance across devices. FLAME features (i) user-centered FL training utilizing the time alignment across devices from the same user; (ii) accuracy- and efficiency-aware device selection; and (iii) model personalization to devices. We also present an FL evaluation testbed with realistic energy drain and network bandwidth profiles, and a novel class-based data partitioning scheme to extend existing HAR datasets to a federated setup. Our experiment results on three multi-device HAR datasets show that FLAME outperforms various baselines by 4.3-25.8% higher F1 score, 1.02-2.86x greater energy efficiency, and up to 2.06x speedup in convergence to target accuracy through fair distribution of the FL workload.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 41 of its 77 references that are in the map. First posted on arXiv 17 Feb 2022.

Builds on

41 of 77 references.

References · 41

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29 more
  1. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  2. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  3. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  4. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  5. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  6. 2019Fair Resource Allocation in Federated Learningcited by 389
  7. 2020Adaptive Personalized Federated Learningcited by 353
  8. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  9. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  10. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  11. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 303
  12. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 2; 2025: 3; 2026: 1.

8 papers.

  1. 2024Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generationcited by 9
  2. 2022SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligencecited by 7
  3. 2022Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCscited by 3
  4. 2025FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalitiescited by 3
  5. 2025Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontierscited by 3
  6. 2024Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clientscited by 1
  7. 2025Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensorscited by 0
  8. 2026FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learningcited by 0