aiqbench analysis
This paper advances federated learning theory by deriving gradient heterogeneity bounds directly from statistical regression models rather than relying on conservative, a priori assumptions. The authors demonstrate that robust convergence is achievable even in the presence of adversarial clients and label noise, provided specific conditions on aggregator coefficients are met. This work offers rigorous theoretical guarantees for deploying federated regression systems where data distribution shifts and malicious updates are concerns.
On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression
arXiv:2609.25705v1 Announce Type: cross 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 $(f,\kappa)$-robust aggregator with coefficient $\kappa = O(f/n)$, 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.
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 $(f,\kappa)$-robust aggregator with coefficient $\kappa = O(f/n)$, 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.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2609.25705 [stat.ML]
(or arXiv:2609.25705v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2609.25705 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 235 words
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