Expected Gain-based Escalation in Vertical Federated Learning

• URL: http://arxiv.org/abs/2606.31331v1
• Date: Tue, 30 Jun 2026 08:32:53 GMT
• Title: Expected Gain-based Escalation in Vertical Federated Learning
• Abstract summary: Collaborative inference can improve predictive performance by integrating complementary information across agents.<n>Applying collaborative fusion to every sample can incur unnecessary communication and computational overhead.<n>We study selective escalation in a two-round VFL inference protocol.
• Score: 6.43987569868938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative inference can improve predictive performance by integrating complementary information across agents, but applying collaborative fusion to every sample can incur unnecessary communication and computational overhead. This trade-off is particularly relevant in vertical federated learning (VFL), where clients observe different views of the same sample and fusion typically requires transmitting intermediate representations to a server. We study selective escalation in a two-round VFL inference protocol, in which a low-cost first round produces a prediction from client posteriors and a second embedding-fusion round is invoked only when it is expected to improve the final decision. We formulate routing as expected-gain score estimation: a sample is escalated when a predicted improvement in correctness justifies the additional communication. The proposed analytical score combines a calibrated pooled posterior with classwise reliability estimates of the VFL model, both obtained from held-out calibration data, yielding an interpretable router that requires no separately trained routing network. Experiments on multi-view classification benchmarks, including controlled test--time view degradation settings, show that the proposed router improves the communication-accuracy trade-off over confidence-, learned-gain-, and deferral-based baselines.

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