Efficiently Distributed Federated Learning

arXiv:2609.19972 · cs.PF, cs.AI, cs.DC · Submitted 2026-09-17 · Read on arXiv

cs.PF, cs.AI, cs.DC

Submitted: 2026-09-17

Updated: 2026-09-17

Journal ref: Euro-Par 2023: Parallel Processing Workshops - Euro-Par 2023 International Workshops, Limassol, Cyprus, August 28 - September 1, 2023, Revised Selected Papers, Part II

DOI: 10.1007/978-3-031-48803-0_40

Code: https://github.com/alpha-unito/FastFederatedLearning

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Federated Learning — 22× in this paper · explained in 14 episodes
• Machine Learning — 10× in this paper
• Clustering — 3× in this paper · explained in 2 episodes
• Distributed Machine Learning — 3× in this paper
• Federated Learning (FL) — 3× in this paper · explained in 7 episodes
• Core — 2× in this paper · explained in 2 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Non-IID Data — 2× in this paper · explained in 2 episodes
• alpha — 2× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• In Edge Inference — 1× in this paper

Sources

• Flower: A Friendly Federated Learning Research Framework
• FedML: A Research Library and Benchmark for Federated Machine Learning
• OpenFL: An open-source framework for Federated Learning
• NVIDIA FLARE: Federated Learning from Simulation to Real-World

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