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Causal Influence in Federated Edge Inference
To evaluate how each agent influences the overall decision, we adopt a causal framework in order to distinguish the actual influence of agents from mere correlations within the decision-making process.
On the Fusion Strategies for Federated Decision Making
We consider the problem of information aggregation in federated decision making, where a group of agents collaborate to infer the underlying state of nature without sharing their private data with the central processor or each other.
On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference
We study the asymptotic learning rates under linear and log-linear combination rules of belief vectors in a distributed hypothesis testing problem.
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