An Investigation of How Neural Networks Learn from the Experiences of Peers Through Periodic Weight Averaging

We investigate a method for cooperative learning called weighted average model fusion that enables neural networks to learn from the experiences of other networks, as well as from their own experiences. Modern machine learning methods have focused predominantly on learning from direct training, but many situations exist where the data cannot be aggregated, rendering direct learning impossible. However, we show that the simple approach of averaging weights with peer neural networks at periodic intervals enables neural networks to learn from second hand experiences. We analyze the effects that several meta-parameters have on model fusion to provide deeper insights into how they affect cooperative learning in a variety of scenarios.