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While federated learning (FL) enables privacy-preserving model training across decentralized devices, its performance is often hampered by non-independent and identically distributed (non-i.i.d.) data and high communication overhead. This paper introduces FedIncSparse*, a new FL framework that addresses these dual challenges through top-K percent sparse delta updates. This approach combines two key advantages: delta transmission slashes communication volume by sending only parameter changes, while incremental updates prevent client drift by gradually refining the global model. We provide robust convergence guarantees for both convex and non-convex objectives. Evaluations on standard benchmarks (MNIST, Fashion-MNIST, CIFAR-10) show FedIncSparse achieves superior accuracy, faster convergence, and up to 35% lower communication costs than FedAvg and FedProx. Furthermore, it delivers performance comparable to FedZip but with reduced computational demands. (Code is available at: https://github.com/dongld-2020/FedIncSparse .)

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

  • 会议论文

  • 发表日期：

    2026年

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  • Duy-Dong Le,
  • Duy-Thanh Huynh,
  • Tuong-Nguyen Huynh

摘要

While federated learning (FL) enables privacy-preserving model training across decentralized devices, its performance is often hampered by non-independent and identically distributed (non-i.i.d.) data and high communication overhead. This paper introduces FedIncSparse*, a new FL framework that addresses these dual challenges through top-K percent sparse delta updates. This approach combines two key advantages: delta transmission slashes communication volume by sending only parameter changes, while incremental updates prevent client drift by gradually refining the global model. We provide robust convergence guarantees for both convex and non-convex objectives. Evaluations on standard benchmarks (MNIST, Fashion-MNIST, CIFAR-10) show FedIncSparse achieves superior accuracy, faster convergence, and up to 35% lower communication costs than FedAvg and FedProx. Furthermore, it delivers performance comparable to FedZip but with reduced computational demands. (Code is available at: https://github.com/dongld-2020/FedIncSparse .)