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About this paper
Meriem Arbaoui, Mohamed-el-Amine Brahmia, Abdellatif Rahmoun, Mourad Zghal
This work introduces a comprehensive, multi-level taxonomy for Federated Learning (FL) challenges and solutions, with a particular focus on aggregation techniques. It provides practical experimental insights into algorithm performance under various real-world conditions and outlines future research frontiers to guide the field.
Key insights
- Federated Learning aggregation is a crucial, often underexplored, aspect whose architectural features, synchronization modes, and motivations profoundly impact overall system performance and robustness.
- Advanced aggregation strategies, such as FedBN and Power-of-Choice, consistently demonstrate superior resilience and adaptability compared to basic FedAvg in handling data and system heterogeneity.
- Optimal FL performance is highly sensitive to the number of participating clients, the type and degree of data distribution skew, available system resources, and deep learning hyperparameters like local iterations and learning rates.
Summary by alphaXiv.
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