mlsanityData to 7 Oct 2026

FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots

arXiv 2101.03705 ↗First posted 11 Jan 2021cs.LGcited by 18

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Abstract (arXiv)

Smartphones, autonomous vehicles, and the Internet-of-things (IoT) devices are considered the primary data source for a distributed network. Due to a revolutionary breakthrough in internet availability and continuous improvement of the IoT devices capabilities, it is desirable to store data locally and perform computation at the edge, as opposed to share all local information with a centralized computation agent. A recently proposed Machine Learning (ML) algorithm called Federated Learning (FL) paves the path towards preserving data privacy, performing distributed learning, and reducing communication overhead in large-scale machine learning (ML) problems. This paper proposes an FL model by monitoring client activities and leveraging available local computing resources, particularly for resource-constrained IoT devices (e.g., mobile robots), to accelerate the learning process. We assign a trust score to each FL client, which is updated based on the client's activities. We consider a distributed mobile robot as an FL client with resource limitations either in memory, bandwidth, processor, or battery life. We consider such mobile robots as FL clients to understand their resource-constrained behavior in a real-world setting. We consider an FL client to be untrustworthy if the client infuses incorrect models or repeatedly gives slow responses during the FL process. After disregarding the ineffective and unreliable client, we perform local training on the selected FL clients. To further reduce the straggler issue, we enable an asynchronous FL mechanism by performing aggregation on the FL server without waiting for a long period to receive a particular client's response.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 20 of its 29 references that are in the map. First posted on arXiv 11 Jan 2021.

Builds on

20 of 29 references.

References · 20

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Towards Federated Learning at Scale: System Designcited by 826
  3. 2018LEAF: A Benchmark for Federated Settingscited by 780
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  5. 2019Asynchronous Federated Optimizationcited by 264
  6. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  7. 2020Threats to Federated Learning: A Surveycited by 171
  8. 2017Asynchronous Decentralized Parallel Stochastic Gradient Descentcited by 159
  9. 2018Mitigating Sybils in Federated Learning Poisoningcited by 158
  10. 2019Federated Learning for Emoji Prediction in a Mobile Keyboardcited by 115
  11. 2018A generic framework for privacy preserving deep learningcited by 103
  12. 2016Asynchronous Stochastic Gradient Descent with Delay Compensationcited by 98
8 more
  1. 2018Federated Learning for Keyword Spottingcited by 95
  2. 2019Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation Systemcited by 79
  3. 2020FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learningcited by 24
  4. 2019Federated Learning for Ranking Browser History Suggestionscited by 23
  5. 2020Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-artcited by 19
  6. 2018Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learningcited by 17
  7. 2018LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care datacited by 9
  8. 2020Privacy-Preserving News Recommendation Model Learningcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 7; 2024: 4; 2025: 0; 2026: 1.

18 papers.

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  1. 2022A Survey on Heterogeneous Federated Learningcited by 42
  2. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  3. 2023FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Schedulercited by 13
  4. 2023Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networkscited by 11
  5. 2021FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximationcited by 10
  6. 2022Pisces: Efficient Federated Learning via Guided Asynchronous Trainingcited by 8
  7. 2022Matching DNN Compression and Cooperative Training with Resources and Data Availabilitycited by 4
  8. 2023Trustworthy Federated Learning: A Surveycited by 4
  9. 2022Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learningcited by 3
  10. 2023A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computingcited by 3
  11. 2023FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizercited by 2
  12. 2024Combining Federated Learning and Control: A Surveycited by 2
6 more
  1. 2023Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Datacited by 1
  2. 2024Context-Aware Orchestration of Energy-Efficient Gossip Learning Schemescited by 1
  3. 2024TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiencycited by 1
  4. 2026Can Optimal Transport Improve Federated Inverse Reinforcement Learning?cited by 1
  5. 2022Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learningcited by 0
  6. 2024Dependable Distributed Training of Compressed Machine Learning Modelscited by 0