arXiv Artificial Intelligence

Efficiently Distributed Federated Learning

3 hours ago 18.09.2026 07:00 👁 2 views
Efficiently Distributed Federated Learning

Quick summary

arXiv:2609.19972v1 Announce Type: cross Abstract: 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 cl

Key takeaways

  • arXiv:2609.19972v1 Announce Type: cross Abstract: 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).

Why it matters

“Efficiently Distributed Federated Learning” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗