Decentralized Machine Unlearning under Client Disconnections

Decentralized machine unlearning seeks to remove the influence of a client without centralized coordination. A distinct challenge arises when a *retained client* becomes unavailable during unlearning, as its learned contribution must remain part of the desired model. We propose a collaborative decentralized unlearning framework that addresses this setting by reconstructing the counterfactual learning dynamics. For active retained clients, we adapt trajectory-based Taylor–L-BFGS reconstruction to reduce direct gradient evaluations. For a disconnected retained client, its former neighbors maintain distributed *ghost replicas* that reconstruct both its missing local updates and its participation in decentralized mixing using finite-step consensus over the active network. We analyze the resulting algorithm from an optimization-dynamics perspective, showing that its deviation from counterfactual retraining decomposes into gradient-reconstruction and ghost-reconstruction errors, with the latter decreasing with the gossip budget and network mixing quality. Experiments show that the proposed method removes the contribution targeted for unlearning while preserving that of an unavailable client, closely approaching oracle retraining.