Local SGD: Unified Theory and New Efficient Methods

by   Eduard Gorbunov, et al.

We present a unified framework for analyzing local SGD methods in the convex and strongly convex regimes for distributed/federated training of supervised machine learning models. We recover several known methods as a special case of our general framework, including Local-SGD/FedAvg, SCAFFOLD, and several variants of SGD not originally designed for federated learning. Our framework covers both the identical and heterogeneous data settings, supports both random and deterministic number of local steps, and can work with a wide array of local stochastic gradient estimators, including shifted estimators which are able to adjust the fixed points of local iterations for faster convergence. As an application of our framework, we develop multiple novel FL optimizers which are superior to existing methods. In particular, we develop the first linearly converging local SGD method which does not require any data homogeneity or other strong assumptions.



page 7

page 15


Minibatch vs Local SGD for Heterogeneous Distributed Learning

We analyze Local SGD (aka parallel or federated SGD) and Minibatch SGD i...

Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

We study the optimization aspects of personalized Federated Learning (FL...

Is Local SGD Better than Minibatch SGD?

We study local SGD (also known as parallel SGD and federated averaging),...

Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Federated learning (FL) has emerged as a prominent distributed learning ...

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

Decentralized stochastic optimization methods have gained a lot of atten...

Better Communication Complexity for Local SGD

We revisit the local Stochastic Gradient Descent (local SGD) method and ...

Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning

Federated learning is one of the important learning scenarios in distrib...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.