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Life sciences · Preprint
arXiv · September 4, 2026
Raises a question worth testing. It does not answer one.
This preprint proposes FedSWE, a novel federated learning algorithm designed to handle non-stationary and heterogeneous client availability without requiring prior knowledge of availability dynamics. The authors provide theoretical convergence guarantees and report numerical experiments on real-world datasets, but the work has not undergone peer review and does not address clinical or applied hard outcomes.
Preprint. Intervention: FedSWE: a federated learning algorithm with three novel components: compensation for missed computations, stabilization and diffusion of global updates, and implicit gossiping for even mixing of local updates. Compared with: Standard FedAvg (federated averaging).
FedSWE introduces algorithmic structures to compensate for missed computations and stabilize global updates despite non-stationary client availability dynamics Compared with standard FedAvg, FedSWE adds light additional memory and computation overhead Theoretical analysis shows FedSWE converges to a stationary point of non-convex objectives with linear speedup in certain special cases
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This is a preprint proposing a novel algorithmic framework for federated learning without peer-reviewed validation, reporting theoretical convergence analysis and numerical experiments rather than clinical or applied hard outcomes.
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Due to resource constraints or external and internal uncertainties, clients in real-world federated learning systems are often intermittently available edge devices. In highly dynamic environments, the parameter server lacks prior real-time knowledge of clients' availability, making it challenging to adapt traditional federated learning algorithms to be resilient to uncertainties in client availability. If not carefully addressed, complex client availability can introduce significant bias, potentially harming the performance of the trained model. Most prior work either fails to account for non-stationary client availability dynamics or demands significant memory and computational overhead. This paper aims to develop efficient federated learning algorithms that are provably resilient to heterogeneous and non-stationary stochastic client availability. We propose FedSWE, which admits novel algorithmic structures to (i) compensate for missed computations, (ii) stabilize and diffuse the global updates over rounds, and (iii) evenly mix the local updates through implicit gossiping, despite being agnostic to non-stationary dynamics. Compared with the standard FedAvg, FedSWE introduces light additional memory and computation overhead. We show that FedSWE converges to a stationary point of non-convex objectives while achieving the desired linear speedup property in certain special cases. We corroborate our analysis with numerical experiments over diversified client unavailability dynamics on real-world data sets.
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