Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning

Vertical federated learning (VFL) is an emerging collaborative machine learning paradigm to facilitate the utilization of private features distributed across multiple parties. During the inference process of VFL, the involved parties need to upload their local embeddings to be aggregated for the final prediction. Despite its remarkable performances, the inference process of the current VFL system is vulnerable to the strategic behavior of involved parties, as they could easily change the uploaded local embeddings to exert direct influences on the prediction result. In a representative case study of federated recommendation, we find the allocation of display opportunities to be severely disrupted due to the parties' preferences in display content. In order to elicit the true local embeddings for VFL system, we propose a distribution-based penalty mechanism to detect and penalize the strategic behaviors in collaborative inference. As the key motivation of our design, we theoretically prove the power of constraining the distribution of uploaded embeddings in preventing the dishonest parties from achieving higher utility. Our mechanism leverages statistical two-sample tests to distinguish whether the distribution of uploaded embeddings is reasonable, and penalize the dishonest party through deactivating her uploaded embeddings. The resulted mechanism could be shown to admit truth-telling to converge to a Bayesian Nash equilibrium asymptotically under mild conditions. The experimental results further demonstrate the effectiveness of the proposed mechanism to reduce the dishonest utility increase of strategic behaviors and promote the truthful uploading of local embeddings in inferences.