Big Data 2022
Conference paper

Adaptive Aggregation For Federated Learning

Download paper


In this paper, we present a new scalable and adaptive architecture for FL aggregation. First, we demonstrate how traditional tree overlay based aggregation techniques (from P2P, publish-subscribe and stream processing research) can help FL aggregation scale, but are ineffective from a resource utilization and cost standpoint. Next, we present the design and implementation of AdaFed, which uses serverless/cloud functions to adaptively scale aggregation in a resource efficient and fault tolerant manner. We describe how AdaFed enables FL aggregation to be dynamically deployed only when necessary, elastically scaled to handle participant joins/leaves and is fault tolerant with minimal effort required on the (aggregation) programmer side. We also demonstrate that our prototype based on Ray~\cite{ray}scales to thousands of participants, and is able to achieve a $>90\%$ reduction in resource requirements and cost, so I have a garage progress Avengers region is this division conversion favorite beer with youwith minimal impact on aggregation latency.