Private Collaborative Edge Inference via Over-the-Air Computation
The work addresses collaborative inference at wireless edges where clients train independent models locally and query them in parallel for a decision. It proposes three methods—Belief Averaging, Weighted Belief Averaging, and Majority Voting—all utilizing Over-the-Air Computation (OAC) to aggregate client information efficiently. These methods achieve better accuracy than traditional approaches while ensuring privacy through differential privacy.