  Federated Learning (FL) typically involves a large-scale, distributed system
with individual user devices/servers training models locally and then
aggregating their model updates on a trusted central server. Existing systems
for FL often use an always-on server for model aggregation, which can be
inefficient in terms of resource utilization. They may also be inelastic in
their resource management. This is particularly exacerbated when aggregating
model updates at scale in a highly dynamic environment with varying numbers of
heterogeneous user devices/servers.
  We present LIFL, a lightweight and elastic serverless cloud platform with
fine-grained resource management for efficient FL aggregation at scale. LIFL is
enhanced by a streamlined, event-driven serverless design that eliminates the
individual heavy-weight message broker and replaces inefficient container-based
sidecars with lightweight eBPF-based proxies. We leverage shared memory
processing to achieve high-performance communication for hierarchical
aggregation, which is commonly adopted to speed up FL aggregation at scale. We
further introduce locality-aware placement in LIFL to maximize the benefits of
shared memory processing. LIFL precisely scales and carefully reuses the
resources for hierarchical aggregation to achieve the highest degree of
parallelism while minimizing the aggregation time and resource consumption. Our
experimental results show that LIFL achieves significant improvement in
resource efficiency and aggregation speed for supporting FL at scale, compared
to existing serverful and serverless FL systems.
