Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis

arXiv:2609.15681 · cs.DC, cs.AI · Submitted 2026-09-14 · Read on arXiv

cs.DC, cs.AI

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 20 pages, 10 figures, 3 tables, published in International Journal of Machine Learning and Cybernetics, this version is largely before peer review revisions

Journal ref: Soudan, B., Abbas, S., Kubba, A. et al. Scalability and performance evaluation of federated learning frameworks: a comparative analysis. Int. J. Mach. Learn. & Cyber. 16, 3329-3343 (2025)

DOI: 10.1007/s13042-024-02453-4

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

Terminology

• Federated Learning — 51× in this paper · explained in 14 episodes
• Accuracy — 47× in this paper · explained in 1 episode
• Aggregation — 18× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• Federated Transfer Learning — 5× in this paper
• Transfer Learning — 5× in this paper · explained in 10 episodes
• Federated Learning (FL) — 4× in this paper · explained in 7 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Edge Devices — 3× in this paper · explained in 2 episodes
• Vertical Federated Learning — 3× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Differential Privacy — 2× in this paper · explained in 4 episodes
• Horizontal Federated Learning — 2× in this paper
• Actor — 1× in this paper · explained in 3 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Byzantine Fault-Tolerant Aggregation — 1× in this paper
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Decentralized Federated Learning — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes

Sources

• Flower: A Friendly Federated Learning Research Framework
• FedML: A Research Library and Benchmark for Federated Machine Learning
• Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning
• Practical Secure Aggregation for Federated Learning on User-Held Data
• Vertical Federated Learning: Concepts, Advances and Challenges
• Cross-Silo Federated Learning: Challenges and Opportunities
• Learning to Detect Malicious Clients for Robust Federated Learning

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