Abstract: Distributed minimax optimization is essential for robust federated learning, offering resiliency against the variability in data distribution. Most previous works focus only on learning ...

Abstract: The gradient descent bit-flipping with momentum (GDBF-w/M) and probabilistic GDBF-w/M (PGDBF-w/M) algorithms significantly improve the decoding performance of the bit-flipping (BF) algorithm ...