Jayashree V. Bagade

Optimization Stability in Federated Learning under Non-IID Data: A Comparative Study of FedAvg and FedProx with Deep Convolutional Neural Network Architectures

A thorough comparison between two well-known federated optimization algorithms, FedAvg and FedProx, and three popular deep convolutional neural network architectures such as ResNet18, VGG16 and VGG19 demonstrates that enforcing strong federated optimization coupled with fitting the appropriate deep convolutional architectures could provide a more reliable way of learning in decentralized settings.