HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning
arXiv:2508.06692v2 Announce Type: replace Abstract: Federated learning systems typically allocate gradient compression by link speed. This is sensible when bandwidth and data informativeness align. However, under non-IID data, these signals often decorrelate or invert. A bandwidth-driven allocator then risks compressing the most informative gradients hardest. We propose HeteRo-Select, a framework that replaces bandwidth with a per-client informativeness score as the primary driver of compression. The score jointly governs three decisions per round: client selection, compression ratio, and serv
The increasing scale and complexity of federated learning systems, especially with non-IID data, necessitate more efficient and intelligent resource allocation methods.
This research optimizes federated learning by prioritizing data informativeness over mere technical factors, potentially accelerating AI development and deployment in diverse, decentralized environments.
Federated learning systems can now make more intelligent decisions about which data to prioritize, improving model accuracy and efficiency, especially in scenarios with heterogeneous data sources.
- · AI developers
- · Organizations with distributed data
- · Edge computing providers
- · Inefficient federated learning systems
- · Bandwidth-constrained organizations
Improved performance and broader applicability of federated learning models.
Increased adoption of federated learning in privacy-sensitive sectors due to enhanced efficiency.
New competitive advantages for companies that effectively leverage distributed, privacy-preserving AI development.
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Read at arXiv cs.LG