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A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions

arXiv 2605.11010 ↗First posted 10 May 2026cs.LGcited by 0

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Abstract (arXiv)

Federated Learning has emerged as a transformative paradigm for collaborative machine learning across distributed environments. However, its performance is strongly influenced by the aggregation strategy used to combine local model updates at the server, which directly affects learning performance, robustness, and system behavior. This work presents a comprehensive experimental comparison of widely used federated aggregation strategies under both homogeneous and heterogeneous data distributions. Using benchmark image classification datasets, we analyze how different aggregation mechanisms respond to varying degrees of data heterogeneity, examining their impact on centralized accuracy and loss, and system-level efficiency metrics, including aggregation, training, and communication time. The results demonstrate that aggregation strategies exhibit distinct trade-offs across datasets and data distributions, with their effectiveness varying according to dataset characteristics and operating conditions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 40 references that are in the map. First posted on arXiv 10 May 2026.

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References · 27

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15 more
  1. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  2. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 160
  3. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  4. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  5. 2020Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcited by 52
  6. 2020Throughput-Optimal Topology Design for Cross-Silo Federated Learningcited by 39
  7. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  8. 2020Network-Aware Optimization of Distributed Learning for Fog Computingcited by 27
  9. 2021Differential Privacy Meets Federated Learning under Communication Constraintscited by 16
  10. 2022Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropoutcited by 12
  11. 2023Zero-Knowledge Proof-based Practical Federated Learning on Blockchaincited by 10
  12. 2022Over-the-Air Federated Learning with Joint Adaptive Computation and Power Controlcited by 9
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  14. 2025FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learningcited by 3
  15. 2025Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clusteringcited by 2

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