# Just-in-Time Aggregation for Federated Learning

arXiv: https://arxiv.org/abs/2208.09740 · HTML: https://mlsanity.com/paper/2208.09740/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2022.

## Built on by (most cited first, 5 of 5)

- [FedLesScan: Mitigating Stragglers in Serverless Federated Learning](https://mlsanity.com/paper/2211.05739/) (2022)
- [FLIPS: Federated Learning using Intelligent Participant Selection](https://mlsanity.com/paper/2308.03901/) (2023)
- [LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning](https://mlsanity.com/paper/2405.10968/) (2024)
- [Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning](https://mlsanity.com/paper/2402.07295/) (2024)
- [Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments](https://mlsanity.com/paper/2404.14033/) (2024)

## Abstract (arXiv)

The increasing number and scale of federated learning (FL) jobs necessitates resource efficient scheduling and management of aggregation to make the economics of cloud-hosted aggregation work. Existing FL research has focused on the design of FL algorithms and optimization, and less on the efficacy of aggregation. Existing FL platforms often employ aggregators that actively wait for model updates. This wastes computational resources on the cloud, especially in large scale FL settings where parties are intermittently available for training. In this paper, we propose a new FL aggregation paradigm -- "just-in-time" (JIT) aggregation that leverages unique properties of FL jobs, especially the periodicity of model updates, to defer aggregation as much as possible and free compute resources for other FL jobs or other datacenter workloads. We describe a novel way to prioritize FL jobs for aggregation, and demonstrate using multiple datasets, models and FL aggregation algorithms that our techniques can reduce resource usage by 60+% when compared to eager aggregation used in existing FL platforms. We also demonstrate that using JIT aggregation has negligible overhead and impact on the latency of the FL job.