mlsanityData to 7 Oct 2026

A Framework for testing Federated Learning algorithms using an edge-like environment

arXiv 2407.12980 ↗First posted 17 Jul 2024cs.LG cs.AI cs.DCcited by 0

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Abstract (arXiv)

Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which involves placing computer workloads (both hardware and software) as close as possible to the edge, where the data is being created and where actions are occurring, enabling faster response times, greater data privacy, and reduced data transfer costs. However, due to the heterogeneous data distributions/contents of clients, it is non-trivial to accurately evaluate the contributions of local models in global centralized model aggregation. This is an example of a major challenge in FL, commonly known as data imbalance or class imbalance. In general, testing and assessing FL algorithms can be a very difficult and complex task due to the distributed nature of the systems. In this work, a framework is proposed and implemented to assess FL algorithms in a more easy and scalable way. This framework is evaluated over a distributed edge-like environment managed by a container orchestration platform (i.e. Kubernetes).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 46 references that are in the map. First posted on arXiv 17 Jul 2024.

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References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Going Deeper with Convolutionscited by 4,231
  3. 2020Adaptive Federated Optimizationcited by 1,036
  4. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  5. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  6. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  7. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 395
  8. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  9. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  10. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Drivingcited by 26
  11. 2020SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft IIcited by 22
  12. 2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTcited by 12
2 more
  1. 2022A Fair Federated Learning Framework With Reinforcement Learningcited by 6
  2. 2022DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learningcited by 1

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