arXiv:2602.03357v2 Announce Type: replace Abstract: This paper proposes FedNMap, a normal map-based method for composite federated learning, where the objective consists of a smooth loss and a possibly nonsmooth regularizer. FedNMap leverages a normal map-based update scheme to handle the nonsmooth term and incorporates a local correction strategy to mitigate the impact of data heterogeneity across clients. Under standard assumptions, including smooth local losses, weak convexity of the regularizer, and bounded stochastic gradient variance, FedNMap achieves linear speedup with respect to both
The paper addresses a core challenge in federated learning – achieving efficiency and robustness in models trained across disparate data sources without centralizing data.
Improved federated learning algorithms enhance the practical application of AI in privacy-sensitive and distributed environments, impacting various industries leveraging decentralized data.
This research provides a more efficient and robust method for federated learning, potentially accelerating its adoption and improving the performance of AI systems trained on heterogeneous datasets.
- · AI developers
- · Healthcare sector
- · Financial services
- · Privacy-focused organizations
- · Centralized data processing models
- · Less efficient federated learning algorithms
Increased adoption and improved performance of federated learning applications.
Enhanced privacy-preserving AI solutions become more deployable across sensitive domains.
Accelerated AI development in sectors with strict data locality or privacy requirements, potentially impacting market dominance for centralized data providers.
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Read at arXiv cs.LG