FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation

no code implementations • 29 Sep 2023 • Alessio Maritan, Subhrakanti Dey, Luca Schenato

Federated learning is a distributed learning framework that allows a set of clients to collaboratively train a model under the orchestration of a central server, without sharing raw data samples.

Federated Learning Privacy Preserving

Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus

no code implementations • 13 May 2023 • Alessio Maritan, Ganesh Sharma, Luca Schenato, Subhrakanti Dey

This paper considers the problem of distributed multi-agent learning, where the global aim is to minimize a sum of local objective (empirical loss) functions through local optimization and information exchange between neighbouring nodes.

Distributed Optimization Federated Learning +1