Federated Swarm Optimization for Privacy-Preserving Parameter Tuning in Networked Learning Systems

• Usman Javed

  Mehran University of Engineering and Technology, Department of Telecommunication Engineering, Jamshoro–Hyderabad Road, Jamshoro, Sindh, Pakistan

  Author

Networked learning systems increasingly operate over distributed data sources that cannot be centralized due to regulatory, organizational, or technical constraints. At the same time, the performance of such systems is sensitive to parameter choices at both the model and system levels, including learning rates, regularization strengths, aggregation weights, and communication schedules. Conventional parameter tuning procedures that rely on centralized access to validation data or repeated global retraining are often incompatible with privacy and communication constraints. Swarm-based metaheuristics provide a flexible mechanism for exploring high-dimensional parameter spaces, yet standard designs assume globally visible objective evaluations. This paper examines a federated swarm optimization strategy for parameter tuning in networked learning systems where data remain local and only carefully controlled summaries are exchanged. The approach conceptualizes each client as an autonomous optimizer that evaluates candidate parameter configurations on local tasks while contributing to a collective search process mediated by a coordinating server. Privacy is addressed by combining aggregation mechanisms with controlled perturbations of shared statistics, while communication costs are managed by sparse and event-driven exchanges of swarm information. The study presents a formal problem formulation, an algorithmic design integrating federated interaction patterns with swarm dynamics, and an analysis of privacy and convergence properties under simplified assumptions on local loss functions and network connectivity. Numerical considerations relevant to practical deployment, such as dimensionality, parameter constraints, and sensitivity to hyperparameters of the swarm itself, are discussed as part of a broader examination of the applicability of federated swarm optimization to privacy-aware networked learning.

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