• Computer Science > Machine Learning [Submitted on 25 Feb 2026] Title:JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning View PDF HTML (experimental)Abstract:Differentially private federated learning faces a fundamental tension: privacy protection mechanisms that safeguard client data simultaneously create quantifiable privacy costs that discourage participation, undermining the collaborative training process • Existing incentive mechanisms rely on unbiased client selection, forcing servers to compensate even the most privacy-sensitive clients (“privacy stragglers”), leading to systemic inefficiency and suboptimal resource allocation • We introduce JSAM (Joint client Selection and privacy compensAtion Mechanism), a Bayesian-optimal framework that simultaneously optimizes client selection probabilities and privacy compensation to maximize training effectiveness under budget constraints • Our approach transforms a complex 2N-dimensional optimization problem into an efficient three-dimensional formulation through novel theoretical characterization of optimal selection strategies • We prove that servers should

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  • Computer Science > Machine Learning [Submitted on 25 Feb 2026] Title:JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning View PDF HTML (experimental)Abstract:Differentially private federated learning faces a fundamental tension: privacy protection mechanisms that safeguard client data simultaneously create quantifiable privacy costs that discourage participation, undermining the collaborative training process. Existing incentive mechanisms rely on unbiased client selection, forcing servers to compensate even the m

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