# LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning

arXiv: https://arxiv.org/abs/2405.10968 · HTML: https://mlsanity.com/paper/2405.10968/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 59 references that are in the map.
- First posted on arXiv 5 May 2024.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://mlsanity.com/paper/2105.11367/) (2021)
- [Papaya: Practical, Private, and Scalable Federated Learning](https://mlsanity.com/paper/2111.04877/) (2021)
- [Peer-to-peer Federated Learning on Graphs](https://mlsanity.com/paper/1901.11173/) (2019)
- [Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider](https://mlsanity.com/paper/2003.03423/) (2020)
- [IBM Federated Learning: an Enterprise Framework White Paper V0.1](https://mlsanity.com/paper/2007.10987/) (2020)
- [FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients](https://mlsanity.com/paper/2201.01601/) (2022)
- [Resource-Efficient Federated Learning](https://mlsanity.com/paper/2111.01108/) (2021)
- [FedLess: Secure and Scalable Federated Learning Using Serverless Computing](https://mlsanity.com/paper/2111.03396/) (2021)
- [Benchmarking, Analysis, and Optimization of Serverless Function Snapshots](https://mlsanity.com/paper/2101.09355/) (2021)
- [Adaptive Aggregation For Federated Learning](https://mlsanity.com/paper/2203.12163/) (2022)
- [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://mlsanity.com/paper/2206.09264/) (2022)
- [Auxo: Efficient Federated Learning via Scalable Client Clustering](https://mlsanity.com/paper/2210.16656/) (2022)
- [Faasm: Lightweight Isolation for Efficient Stateful Serverless Computing](https://mlsanity.com/paper/2002.09344/) (2020)
- [FaaSNet: Scalable and Fast Provisioning of Custom Serverless Container Runtimes at Alibaba Cloud Function Compute](https://mlsanity.com/paper/2105.11229/) (2021)
- [Following the Data, Not the Function: Rethinking Function Orchestration in Serverless Computing](https://mlsanity.com/paper/2109.13492/) (2021)
- [Flame: Simplifying Topology Extension in Federated Learning](https://mlsanity.com/paper/2305.05118/) (2023)
- [Just-in-Time Aggregation for Federated Learning](https://mlsanity.com/paper/2208.09740/) (2022)
- [Mitigating Cold Starts in Serverless Platforms: A Pool-Based Approach](https://mlsanity.com/paper/1903.12221/) (2019)
- [Venn: Resource Management for Collaborative Learning Jobs](https://mlsanity.com/paper/2312.08298/) (2023)

## Built on by (most cited first, 2 of 2)

- [FLStore: Efficient Federated Learning Storage for non-training workloads](https://mlsanity.com/paper/2503.00323/) (2025)
- [Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning](https://mlsanity.com/paper/2604.22072/) (2026)

## Abstract (arXiv)

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.