arXiv:2606.10780v1 Announce Type: cross Abstract: Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with the gradient dimension. This becomes prohibitive for large models and even more pronounced in decentralized federated learning with limited bandwidth and unreliable nodes. Top-K gradient sparsification is an effective approach to reduce communication by transmitting only a few entries of the full gradient, while maintaining competitive model accuracy. Nevertheless, the top-K entries selected by eac
The increasing scale of large models and the proliferation of decentralized federated learning environments necessitate more efficient communication methods for secure gradient aggregation.
Improving efficiency and security in decentralized federated learning is critical for its broader adoption, particularly in sensitive applications and resource-constrained environments.
This advancement enables more practical and secure federated learning implementations by significantly reducing communication overhead while maintaining model accuracy and mitigating data leakage risks.
- · AI developers and researchers
- · Organizations using federated learning
- · Edge computing providers
- · Traditional centralized ML infrastructure
- · Systems highly reliant on massive full gradient transfers
More widespread and efficient deployment of federated learning applications, especially in areas with privacy concerns.
Accelerated development of AI models that can be collaboratively trained without centralizing sensitive data.
Enhanced data privacy and security frameworks for distributed AI, potentially changing regulatory landscapes around data sharing.
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