distributed-optimization

This thesis tackles seven challenges in distributed and federated optimization, introducing methods like ProxSkip and Variance Reduced ProxSkip, and establishing theoretical foundations for communication-efficient, robust, and practical algorithms.

This paper proposes a data-driven elastic-net support vector machine that learns simplex-constrained weights over candidate pinball losses, with a distributed solver for vertically partitioned high-dimensional data. Theoretical guarantees and experiments demonstrate equivalence to centralized training under common initialization.

This paper proposes F2CTO, the first distributed first-order constrained trilevel optimization method for robust coreset selection over distributed networks, with a non-asymptotic convergence guarantee of O(ε^(-3/2)).