Federated learning gains clearer understanding of adversary and data differences
On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression
Machine Learning
Summary
Federated learning lets many devices or clients train a model together without sharing their data. Sometimes, clients have very different data or models, and some may even try to mess up training on purpose. The authors studied how these differences and attacks affect the learning process, especially for common regression tasks. They found a better way to measure and separate these effects, showing when learning will still work despite adversaries.
What this means in practice
- •For data scientists: Design reliable federated regression models that converge despite some clients acting maliciously or having different data.
- •For machine learning engineers: Improve federated learning setups by understanding how initial model differences and data noise influence training stability under adversarial conditions.
A theory result. No direct application yet.
Authors
Leonardo F. Toso, James Anderson, Nirupam Gupta, Rafael Pinot
Abstract
Federated learning (FL) is intrinsically heterogeneous: honest clients may have different data-generating models. On top of that, adversarial clients can make heterogeneity even more pronounced by sharing arbitrary updates. Existing analyses typically control the interaction between statistical heterogeneity and adversarial behavior through gradient-dissimilarity conditions. However, the underlying bound is imposed a priori and may yield conservative guarantees even for least-squares regression. We instead derive the gradient heterogeneity from the statistical model of linear and nonlinear regression with fresh data samples at every round. Our bounds separate heterogeneity among the honest clients' ground-truth model parameters, finite-sample label noise, and initialization. We then demonstrate that, for any $(f,κ)$-robust aggregator with coefficient $κ= O(f/n)$, where $f$ is the number of adversarial clients and $n$ the total number of clients (with $f/n < 1/2$), convergence holds after an explicit sample burn-in.