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AFAFed -- Protocol analysis
The convergence properties of AFAFed under (possibly) non-convex loss functions is guaranteed by a set of new analytical bounds, which formally unveil the impact on the resulting AFAFed convergence rate of a number of Federated Learning (FL) parameters, like, first and second moments of the per-coworker number of consecutive model updates, data skewness, communication packet-loss probability, and maximum/minimum values of the (adaptively tuned) mixing coefficient used for model aggregation.
Gomoku: analysis of the game and of the player Wine
Gomoku, also known as five in a row, is a classical board game, ideally suited for quickly testing novel Artificial Intelligence (AI) techniques.
Why should we add early exits to neural networks?
Deep neural networks are generally designed as a stack of differentiable layers, in which a prediction is obtained only after running the full stack.
