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

arXiv: https://arxiv.org/abs/2101.03705 · HTML: https://mlsanity.com/paper/2101.03705/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 29 references that are in the map.
- First posted on arXiv 11 Jan 2021.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020)
- [Mitigating Sybils in Federated Learning Poisoning](https://mlsanity.com/paper/1808.04866/) (2018)
- [Asynchronous Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1710.06952/) (2017)
- [Federated Learning for Emoji Prediction in a Mobile Keyboard](https://mlsanity.com/paper/1906.04329/) (2019)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [Asynchronous Stochastic Gradient Descent with Delay Compensation](https://mlsanity.com/paper/1609.08326/) (2016)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System](https://mlsanity.com/paper/1901.09888/) (2019)
- [Federated Learning for Ranking Browser History Suggestions](https://mlsanity.com/paper/1911.11807/) (2019)
- [FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning](https://mlsanity.com/paper/2003.09288/) (2020)
- [Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art](https://mlsanity.com/paper/2002.10610/) (2020)
- [Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning](https://mlsanity.com/paper/1811.09904/) (2018)
- [LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data](https://mlsanity.com/paper/1811.12629/) (2018)
- [Privacy-Preserving News Recommendation Model Learning](https://mlsanity.com/paper/2003.09592/) (2020)

## Built on by (most cited first, 18 of 18)

- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2307.09182/) (2023)
- [FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler](https://mlsanity.com/paper/2309.14675/) (2023)
- [Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks](https://mlsanity.com/paper/2301.06646/) (2023)
- [FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation](https://mlsanity.com/paper/2112.11989/) (2021)
- [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://mlsanity.com/paper/2206.09264/) (2022)
- [Matching DNN Compression and Cooperative Training with Resources and Data Availability](https://mlsanity.com/paper/2212.02304/) (2022)
- [Trustworthy Federated Learning: A Survey](https://mlsanity.com/paper/2305.11537/) (2023)
- [Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning](https://mlsanity.com/paper/2210.10311/) (2022)
- [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023)
- [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024)
- [FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer](https://mlsanity.com/paper/2305.01154/) (2023)
- [TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency](https://mlsanity.com/paper/2409.05347/) (2024)
- [Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data](https://mlsanity.com/paper/2312.10411/) (2023)
- [Context-Aware Orchestration of Energy-Efficient Gossip Learning Schemes](https://mlsanity.com/paper/2404.12023/) (2024)
- [Can Optimal Transport Improve Federated Inverse Reinforcement Learning?](https://mlsanity.com/paper/2601.00309/) (2026)
- [Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learning](https://mlsanity.com/paper/2212.02155/) (2022)
- [Dependable Distributed Training of Compressed Machine Learning Models](https://mlsanity.com/paper/2402.14346/) (2024)

## 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.