mlsanityData to 7 Oct 2026

Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones

arXiv 2210.16918 ↗First posted 30 Oct 2022cs.LGcited by 6

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Abstract (arXiv)

Pervasive computing promotes the integration of smart devices in our living spaces to develop services providing assistance to people. Such smart devices are increasingly relying on cloud-based Machine Learning, which raises questions in terms of security (data privacy), reliance (latency), and communication costs. In this context, Federated Learning (FL) has been introduced as a new machine learning paradigm enhancing the use of local devices. At the server level, FL aggregates models learned locally on distributed clients to obtain a more general model. In this way, no private data is sent over the network, and the communication cost is reduced. Unfortunately, however, the most popular federated learning algorithms have been shown not to be adapted to some highly heterogeneous pervasive computing environments. In this paper, we propose a new FL algorithm, termed FedDist, which can modify models (here, deep neural network) during training by identifying dissimilarities between neurons among the clients. This permits to account for clients' specificity without impairing generalization. FedDist evaluated with three state-of-the-art federated learning algorithms on three large heterogeneous mobile Human Activity Recognition datasets. Results have shown the ability of FedDist to adapt to heterogeneous data and the capability of FL to deal with asynchronous situations.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 30 Oct 2022.

Builds on

17 of 32 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2019Towards Federated Learning at Scale: System Designcited by 826
  4. 2017Federated Multi-Task Learningcited by 669
  5. 2020Federated Learning with Matched Averagingcited by 564
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  7. 2019Federated Learning with Personalization Layerscited by 533
  8. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  9. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  10. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  11. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  12. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
5 more
  1. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  2. 2020Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Frameworkcited by 59
  3. 2021A distillation-based approach integrating continual learning and federated learning for pervasive servicescited by 42
  4. 2021A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparisoncited by 31
  5. 2021Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applicationscited by 11

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Cited by year (2026 to 7 Oct)

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

6 papers.

  1. 2023Federated Few-shot Learning for Cough Classification with Edge Devicescited by 1
  2. 2023Horizontal Federated Computer Visioncited by 0
  3. 2025FedFitTech: A Baseline in Federated Learning for Fitness Trackingcited by 0
  4. 2025Parameter-Efficient Fine-Tuning for HAR: Integrating LoRA and QLoRA into Transformer Modelscited by 0
  5. 2026FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Datacited by 0
  6. 2026FedAvg for HAR: Exploring the Tradeoff Between Personalized and Generalization Accuracycited by 0