mlsanityData to 14 Oct 2026

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

arXiv 2405.10968 ↗First posted 5 May 2024cs.DC cs.LGcited by 2

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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.

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

28 of 59 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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16 more
  1. 2020Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Providercited by 66
  2. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  3. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  4. 2021Resource-Efficient Federated Learningcited by 26
  5. 2021FedLess: Secure and Scalable Federated Learning Using Serverless Computingcited by 13
  6. 2021Benchmarking, Analysis, and Optimization of Serverless Function Snapshotscited by 12
  7. 2022Adaptive Aggregation For Federated Learningcited by 10
  8. 2022Pisces: Efficient Federated Learning via Guided Asynchronous Trainingcited by 8
  9. 2022Auxo: Efficient Federated Learning via Scalable Client Clusteringcited by 8
  10. 2020Faasm: Lightweight Isolation for Efficient Stateful Serverless Computingcited by 7
  11. 2021FaaSNet: Scalable and Fast Provisioning of Custom Serverless Container Runtimes at Alibaba Cloud Function Computecited by 7
  12. 2021Following the Data, Not the Function: Rethinking Function Orchestration in Serverless Computingcited by 7
  13. 2023Flame: Simplifying Topology Extension in Federated Learningcited by 7
  14. 2022Just-in-Time Aggregation for Federated Learningcited by 5
  15. 2019Mitigating Cold Starts in Serverless Platforms: A Pool-Based Approachcited by 3
  16. 2023Venn: Resource Management for Collaborative Learning Jobscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025FLStore: Efficient Federated Learning Storage for non-training workloadscited by 0
  2. 2026Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioningcited by 0