Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention
arXiv:2605.11752v2 Announce Type: replace Abstract: Federated learning relies on effective client selection to alleviate the performance degradation caused by data heterogeneity. Most existing methods assume full visibility of all clients at each communication round. However, in large-scale or edge-based deployments, the server can only access a subset of clients due to communication, mobility, or availability constraints, resulting in partial visibility where only a subset of clients is observable for aggregation in each communication round. In this paper, we formulate federated client select
The increasing scale and complexity of federated learning deployments, especially in edge computing scenarios, are bringing the limitations of full client visibility to the forefront, necessitating new algorithmic approaches.
Improving client selection under partial visibility is crucial for scaling federated learning effectively, impacting the performance and deployment viability of AI systems in privacy-preserving and distributed environments.
This research introduces methodologies to make federated learning more robust and efficient in real-world, large-scale, and constrained communication settings, expanding its applicability beyond ideal conditions.
- · Edge AI providers
- · Federated learning platforms
- · Organizations with distributed data
- · Privacy-preserving AI
- · Centralized AI architectures
- · Inefficient federated learning systems relying on full visibility
More resilient and scalable federated learning deployments become possible, particularly at the edge.
Enhanced federated learning capabilities could accelerate the development of localized AI applications and services that respect data sovereignty.
The broader adoption of federated learning could reduce the need for centralized data aggregation, shifting power dynamics in data ownership and AI development.
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