arXiv Machine Learning June 26, 2026 By Farwa Ikram, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino

Quantization in Federated Learning: Methods, Challenges and Future Directions

Read the original on arXiv Machine Learning →

arXiv:2606. 26822v1 Announce Type: new Abstract: Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally constrained by communication bottlenecks, device heterogeneity, and the challenges of training under statistically non-IID data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting

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