Dec 14, 2018 · In this work, we introduce a framework, FedProx, to tackle heterogeneity in federated networks. FedProx can be viewed as a generalization and re ...

Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant ...

We introduce a framework, FedProx, to tackle heterogeneity in federated networks. FedProx can be viewed as a generalization and re-parametrization of FedAvg.

This work introduces a framework, FedProx, to tackle heterogeneity in federated networks, and provides convergence guarantees for this framework when ...

In this work, we introduce a framework, FedProx, to tackle heterogeneity in federated networks, both theoretically and empirically. This repository contains a ...

Federated learning involves training and effec- tively combining machine learning models from distributed partitions of data (i.e., tasks) on edge.

Assumptions. Assumption 1: Bounded Dissimilarity. Assumption 2: Modified Local subproblem is convex. & smooth. Assumption 3: Each local subproblem is solved.

May 13, 2024 · Bibliographic details on Federated Optimization in Heterogeneous Networks.

"Communication-efficient learning of deep networks from decentralized data." AISTATS, 2017. Works well in many settings ! (especially non-convex). 5. At each ...

Federated learning involves training and effectively combining machine learning models from distributed partitions of data (i.e., tasks) on edge devices, ...