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Efficiently Distributed Federated Learning

arXiv 2609.19972 ↗First posted 17 Sep 2026cs.PF cs.AI cs.DCcited by 0

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Abstract (arXiv)

Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federations easily and quickly. Most of these efforts do not consider two main aspects that are key to Machine Learning (ML) software: customizability and performance. This research addresses these issues by implementing an open-source FL framework named FastFederatedLearning (FFL). FFL is implemented in C/C++, focusing on code performance, and allows the user to specify any communication graph between clients and servers involved in the federation, ensuring customizability. FFL is tested against Intel OpenFL, achieving consistent speedups over different computational platforms (x86-64, ARM-v8, RISC-V), ranging from 2.5x and 3.69x. We aim to wrap FFL with a Python interface to ease its use and implement a middleware for different communication backends to be used. We aim to build dynamic federations in which relations between clients and servers are not static, giving life to an environment where federations can be seen as long-time evolving structures and exploited as services.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 21 references that are in the map. First posted on arXiv 17 Sep 2026.

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12 of 21 references.

References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  4. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  5. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  6. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  7. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  8. 2019A Survey on Distributed Machine Learningcited by 188
  9. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
  10. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 80
  11. 2020FLaaS: Federated Learning as a Servicecited by 22
  12. 2023Experimenting with Emerging RISC-V Systems for Decentralised Machine Learningcited by 3

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