fedavg

This paper introduces FLITE (Federated Low-rank Iterative Training Engine), a method for federated fine-tuning that reduces per-client communication to 1,280 floats per round (about 5KB) — an 8718× reduction over full-weight FedAvg — by using a frozen affine mapping network that generates weights from a small trainable latent and a low-rank seed-regenerable factorization, achieving accuracy within 0.5 percentage points of full-weight FedAvg on CIFAR-100 with ResNet-18.