arXiv:2602.01186v2 Announce Type: replace Abstract: Classical Federated Learning relies on a multi-round iterative process of model exchange and aggregation between server and clients, with high communication costs and privacy risks from repeated model transmissions. In contrast, one-shot federated learning (OFL) alleviates these limitations by reducing communication to a single round, thereby lowering overhead and enhancing practical deployability. Nevertheless, most existing one-shot approaches remain either impractical or constrained, for example, they often depend on the availability of a
The proliferation of edge devices and increasing privacy concerns are driving the need for more efficient and secure federated learning methodologies.
This development in one-shot federated learning significantly reduces communication costs and privacy risks, making AI deployment more practical and scalable across distributed datasets.
Traditional iterative federated learning is being challenged by more efficient single-round approaches, enabling broader adoption in sensitive or resource-constrained environments.
- · Edge AI developers
- · Privacy-focused tech companies
- · Distributed computing platforms
- · AI-driven IoT solutions
- · High-latency federated learning platforms
- · Traditional centralized AI training models for sensitive data
Wider adoption of federated learning in sectors like healthcare and finance due to reduced overhead and enhanced privacy.
Decentralization of AI training could lead to new business models and data ownership structures.
Enhanced on-device intelligence fostering a new wave of personalized and secure AI applications, less reliant on cloud processing.
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Read at arXiv cs.LG