FedDec: Peer-to-peer Aided Federated Learning

no code implementations • 11 Jun 2023 • Marina Costantini, Giovanni Neglia, Thrasyvoulos Spyropoulos

We analyze the convergence of FedDec under the assumptions of non-iid data distribution, partial device participation, and smooth and strongly convex costs, and show that inter-agent communication alleviates the negative impact of infrequent communication rounds with the server by reducing the dependence on the number of local updates $H$ from $O(H^2)$ to $O(H)$.

Federated Learning

Pick your Neighbor: Local Gauss-Southwell Rule for Fast Asynchronous Decentralized Optimization

1 code implementation • 15 Jul 2022 • Marina Costantini, Nikolaos Liakopoulos, Panayotis Mertikopoulos, Thrasyvoulos Spyropoulos

In decentralized optimization environments, each agent $i$ in a network of $n$ nodes has its own private function $f_i$, and nodes communicate with their neighbors to cooperatively minimize the aggregate objective $\sum_{i=1}^n f_i$.