Battery-aware federated learning: Challenges and solutions

Abstract

Smartphone battery life is a pivotal factor in consumers' purchasing decisions. Recent years have witnessed a surge in studies focusing on smartphone energy management, with data-driven energy management systems offering solutions to prolong battery life. Federated Learning (FL) emerges as a promising distributed learning algorithm, enabling wireless devices to upload locally trained models, fostering collaborative learning without exposing sensitive data. This paper explores the FL process, particularly the Federated Averaging (FedAvg) approach, which excels in scenarios with homogeneous data. In the era of burgeoning data generation, traditional cloud computing systems face limitations, driving the adoption of Edge Computing (EC), which processes data closer to its source, enhancing response times. To make FL efficient for e-commerce, resource constraints must be addressed. This involves techniques like local updates and model compression, which reduce communication overhead. However, FL brings challenges related to data distribution heterogeneity and privacy concerns. Solutions like differential privacy, encryption, and access control are discussed. In conclusion, this paper presents an overview of smartphone battery life, data-driven energy management, and the potential of FL, emphasizing its relevance in the age of EC. By addressing resource limitations and privacy issues, FL holds promise for efficient data processing.