VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence
This addresses the challenge of secure and efficient distributed machine learning for systems like IoT or federated learning, representing a novel paradigm rather than an incremental improvement.
The paper tackles the problem of decentralized learning in networks with heterogeneous data and potential adversarial agents, introducing the VALID protocol that achieves convergence to a global empirical loss minimizer without adversaries and either detects adversaries or converges to an admissible consensus with them, offering an O(1/T) convergence rate and optimal performance in adversary-free environments.
We introduce the paradigm of validated decentralized learning for undirected networks with heterogeneous data and possible adversarial infiltration. We require (a) convergence to a global empirical loss minimizer when adversaries are absent, and (b) either detection of adversarial presence of convergence to an admissible consensus irrespective of the adversarial configuration. To this end, we propose the VALID protocol which, to the best of our knowledge, is the first to achieve a validated learning guarantee. Moreover, VALID offers an O(1/T) convergence rate (under pertinent regularity assumptions), and computational and communication complexities comparable to non-adversarial distributed stochastic gradient descent. Remarkably, VALID retains optimal performance metrics in adversary-free environments, sidestepping the robustness penalties observed in prior byzantine-robust methods. A distinctive aspect of our study is a heterogeneity metric based on the norms of individual agents' gradients computed at the global empirical loss minimizer. This not only provides a natural statistic for detecting significant byzantine disruptions but also allows us to prove the optimality of VALID in wide generality. Lastly, our numerical results reveal that, in the absence of adversaries, VALID converges faster than state-of-the-art byzantine robust algorithms, while when adversaries are present, VALID terminates with each honest either converging to an admissible consensus of declaring adversarial presence in the network.