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Dionysis Kalogerias

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#machine learning Preprint Sep 2026

FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift

Federated learning (FL) enables collaborative model training without sharing raw data, but its performance degrades under non-IID data and stochastic client participation. Remedies built on classical Federated Averaging (FedAvg) typically presuppose that client participation probabilities are known to the server, which...

Herlock Rahimi, Dionysis Kalogerias · 0 citations

Preprint Aug 2026

Constrained Learning with Universally Learnable Concept Classes

We study constrained statistical learning over infinite-dimensional hypothesis classes in the fully nonconvex setting, and establish universal PACC learnability of the solutions of dual algorithms: Probably Approximately Correct on Constraints, guaranteeing optimality and constraint satisfaction at once. This strengthe...

Herlock Rahimi, Spyridon Pougkakiotis, Dionysis Kalogerias · 0 citations