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.

LIFL 是一种轻量级和弹性的无服务器云平台，通过精细的资源管理实现规模化的 FL 聚合，它通过流线型事件驱动的无服务器设计，以及使用轻量级的 eBPF 代理替代容器化的边车来提高效率，并利用共享内存处理实现高性能通信，进一步引入了 LIFL 中的位局感知布局以最大化共享内存处理的效益，LIFL 通过精确地规模化和资源复用来实现最高程度的并行性，同时将聚合时间和资源消耗最小化。