  Advances in federated learning (FL) algorithms,along with technologies like
differential privacy and homomorphic encryption, have led to FL being
increasingly adopted and used in many application domains. This increasing
adoption has led to rapid growth in the number, size (number of
participants/parties) and diversity (intermittent vs. active parties) of FL
jobs. Many existing FL systems, based on centralized (often single) model
aggregators are unable to scale to handle large FL jobs and adapt to parties'
behavior.
  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 scales to thousands of
participants, and is able to achieve a >90% reduction in resource requirements
and cost, with minimal impact on aggregation latency.
