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This paper presents an adaptive combination strategy for distributed learningover diffusion networks. Since learning relies on the collaborative processingof the stochastic information at the dispersed agents, the overall performancecan be improved by designing combination policies that adjust the weightsaccording to the quality of the data. Such policies are important because theywould add a new degree of freedom and endow multi-agent systems with theability to control the flow of information over their edges for enhancedperformance. Most adaptive and static policies available in the literatureoptimize certain performance metrics related to steady-state behavior, to thedetriment of transient behavior. In contrast, we develop an adaptivecombination rule that aims at optimizing the transient learning performance,while maintaining the enhanced steady-state performance obtained using policiespreviously developed in the literature.

Gramian-Based Adaptive Combination Policies for Diffusion Learning over Networks