Towards cost-effective and resource-aware aggregation at Edge for Federated Learning

Abstract

Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT applications where the aggregator server of FL is in resource-capped edge data centers for reducing communication costs. Existing cloud-based aggregator solutions are resource-inefficient and expensive at the Edge, leading to low scalability and high latency. To address these challenges, this study compares prior and new aggregation methodologies under the changing demands of IoT and Edge applications. This work is the first to propose an adaptive FL aggregator at the Edge, enabling users to manage the cost and efficiency trade-off. An extensive comparative analysis demonstrates that the design improves scalability by up to 4X, time efficiency by 8X, and reduces costs by more than 2X compared to extant cloud-based static methodologies.

10 pages, 12 figures, 4 tables This paper has been accepted at the 2023 IEEE International Conference on Big Data (BigData)

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Array Programming with NumPy
• IBM Federated Learning: an Enterprise Framework White Paper V0.1
• FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
• IP-FL: Incentivized and Personalized Federated Learning

Cited by in corpus (1)

• MeanCache: User-Centric Semantic Caching for LLM Web Services