Abstract:
In this paper, a novel federated learning algorithm for decentralized settings on edge devices—
AlphaFedAvg—is introduced. Using an adaptive learning rate approach based on Lipschitz and Smoothness
parameters, AlphaFedAvg dynamically modifies the learning rate for every node. Through federated averaging, the
approach accomplishes model aggregation, exhibiting enhanced convergence and performance. An extensive test
configuration includes using Kali Linux to simulate network assaults, an ESP32 microcontroller connected to a laptop
equipped with a sound sensor, and Wireshark and Scapy for traffic analysis. The Alpha algorithm offers a privacypreserving
solution by effectively identifying and thwarting network attacks without gaining access to user data. The
algorithm's performance is demonstrated in a comprehensive report generated. Evaluation against IID and non-IID
datasets, such as Edge-IIoTset, and comparison with other models validate AlphaFedAvg's efficacy in federated
learning applications.
