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FedIncSparse: A Federated Learning Framework with Top-K Sparse Delta Transmission and Incremental Updates

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Duy-Dong Le
Duy-Thanh Huynh
Tuong-Nguyen Huynh
Published in
Multi-disciplinary Trends in Artificial Intelligence
Publisher
Springer Nature Singapore

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Abstract

FedIncSparse introduces a novel federated learning framework designed to tackle the challenges of communication efficiency and client drift in non-i.i.d settings. The framework employs top-K sparse delta updates, which significantly reduce communication overhead while maintaining model performance. This approach is rigorously validated through experiments on MNIST, Fashion-MNIST, and CIFAR-10 datasets, demonstrating up to 35% communication reduction compared to traditional methods like FedAvg and FedProx. The framework's convergence analysis accounts for sparsification errors, ensuring robustness. Additionally, the paper explores the trade-offs between performance and efficiency, highlighting FedIncSparse's ability to balance both. The results show that FedIncSparse achieves competitive accuracy with reduced communication costs, making it a promising solution for practical federated learning applications.
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previous chapter Principal Directions-Based Data Classification Optimized by Genetic Algorithms and K-Nearest Neighbors
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Title
FedIncSparse: A Federated Learning Framework with Top-K Sparse Delta Transmission and Incremental Updates
Authors
Duy-Dong Le
Duy-Thanh Huynh
Tuong-Nguyen Huynh
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Multi-disciplinary Trends in Artificial Intelligence

Print ISBN: 978-981-9549-56-6

Electronic ISBN: 978-981-9549-57-3

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-4957-3_15
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