mlsanityData to 7 Oct 2026

Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection

arXiv 2106.07976 ↗First posted 15 Jun 2021cs.LG cs.SY eess.SYcited by 19

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Abstract (arXiv)

Federated learning can be a promising solution for enabling IoT cybersecurity (i.e., anomaly detection in the IoT environment) while preserving data privacy and mitigating the high communication/storage overhead (e.g., high-frequency data from time-series sensors) of centralized over-the-cloud approaches. In this paper, to further push forward this direction with a comprehensive study in both algorithm and system design, we build FedIoT platform that contains FedDetect algorithm for on-device anomaly data detection and a system design for realistic evaluation of federated learning on IoT devices. Furthermore, the proposed FedDetect learning framework improves the performance by utilizing a local adaptive optimizer (e.g., Adam) and a cross-round learning rate scheduler. In a network of realistic IoT devices (Raspberry PI), we evaluate FedIoT platform and FedDetect algorithm in both model and system performance. Our results demonstrate the efficacy of federated learning in detecting a wider range of attack types occurred at multiple devices. The system efficiency analysis indicates that both end-to-end training time and memory cost are affordable and promising for resource-constrained IoT devices. The source code is publicly available at https://github.com/FedML-AI/FedIoT.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 30 of its 45 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

30 of 45 references.

References · 30

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18 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 1; 2023: 3; 2024: 4; 2025: 4; 2026: 2.

19 papers.

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  1. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 126
  2. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  3. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  4. 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 49
  5. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  6. 2023TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Trainingcited by 27
  7. 2021FedAdapt: Adaptive Offloading for IoT Devices in Federated Learningcited by 26
  8. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  9. 2023An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneitycited by 5
  10. 2025C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learningcited by 2
  11. 2024Endangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and Roadsidescited by 1
  12. 2023CADeSH: Collaborative Anomaly Detection for Smart Homescited by 0
7 more
  1. 2024Enhancing IoT Security Against DDoS Attacks through Federated Learningcited by 0
  2. 2024QBI: Quantile-Based Bias Initialization for Efficient Private Data Reconstruction in Federated Learningcited by 0
  3. 2025HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrastcited by 0
  4. 2025An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learningcited by 0
  5. 2025FedOptima: Optimizing Resource Utilization in Federated Learningcited by 0
  6. 2026Toward Resilient 5G Networks: Comparative Analysis of Federated and Centralized Learning for RF Jamming Detectioncited by 0
  7. 2026Client and Training Data Selection for Computationally Efficient Synchronized Federated Learningcited by 0