Fault Tolerant Serverless VFL Over Dynamic Device Environment

Abstract

Vertical Federated learning (VFL) is a class of FL where each client shares the same set of samples but only owns a subset of the features. Usually, VFL assumes perfect hardware and communication capabilities. However, this assumption hinders the broad deployment of VFL, particularly on a network of edge devices, which are heterogeneous in their in-situ capabilities while any device may connect/disconnect from the network over time. To address this gap, we study the test time performance of VFL under dynamic network conditions, which we call DN-VFL. We first formalize DN-VFL, including a message passing distributed inference algorithm, the corresponding risk, and a serverless setup. We develop a novel DN-VFL approach called Multiple Aggregation with Gossip Rounds and Simulated Faults (MAGS) that synthesizes replication, gossiping, and selective feature omission to improve performance significantly over baselines. Furthermore, we propose metrics and extensively analyze MAGS using a simulated sensor network. The results show that naively using VFL for DN-VFL is not the best approach. Rather, MAGS present a better alternative to handle changes in the network during inference.

Tasks

Reproductions