# FLStore: Efficient Federated Learning Storage for non-training workloads

arXiv: https://arxiv.org/abs/2503.00323 · HTML: https://mlsanity.com/paper/2503.00323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 38 of its 77 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 30 of 38)

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- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
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- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
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- [FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers](https://mlsanity.com/paper/2010.05958/) (2020)
- [Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing](https://mlsanity.com/paper/2106.04502/) (2021)
- [IBM Federated Learning: an Enterprise Framework White Paper V0.1](https://mlsanity.com/paper/2007.10987/) (2020)
- [FedSoft: Soft Clustered Federated Learning with Proximal Local Updating](https://mlsanity.com/paper/2112.06053/) (2021)
- [TicTac: Accelerating Distributed Deep Learning with Communication Scheduling](https://mlsanity.com/paper/1803.03288/) (2018)
- [Resource-Efficient Federated Learning](https://mlsanity.com/paper/2111.01108/) (2021)
- [Personalized Federated Learning with Contextualized Generalization](https://mlsanity.com/paper/2106.13044/) (2021)
- [BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture](https://mlsanity.com/paper/2010.07427/) (2020)
- [Amazon SageMaker Autopilot: a white box AutoML solution at scale](https://mlsanity.com/paper/2012.08483/) (2020)
- [Towards Demystifying Serverless Machine Learning Training](https://mlsanity.com/paper/2105.07806/) (2021)
- [FedLess: Secure and Scalable Federated Learning Using Serverless Computing](https://mlsanity.com/paper/2111.03396/) (2021)
- [Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization](https://mlsanity.com/paper/2012.08489/) (2020)
- [Auxo: Efficient Federated Learning via Scalable Client Clustering](https://mlsanity.com/paper/2210.16656/) (2022)

## Abstract (arXiv)

Federated Learning (FL) is an approach for privacy-preserving Machine Learning (ML), enabling model training across multiple clients without centralized data collection. With an aggregator server coordinating training, aggregating model updates, and storing metadata across rounds. In addition to training, a substantial part of FL systems are the non-training workloads such as scheduling, personalization, clustering, debugging, and incentivization. Most existing systems rely on the aggregator to handle non-training workloads and use cloud services for data storage. This results in high latency and increased costs as non-training workloads rely on large volumes of metadata, including weight parameters from client updates, hyperparameters, and aggregated updates across rounds, making the situation even worse. We propose FLStore, a serverless framework for efficient FL non-training workloads and storage. FLStore unifies the data and compute planes on a serverless cache, enabling locality-aware execution via tailored caching policies to reduce latency and costs. Per our evaluations, compared to cloud object store based aggregator server FLStore reduces per request average latency by 71% and costs by 92.45%, with peak improvements of 99.7% and 98.8%, respectively. Compared to an in-memory cloud cache based aggregator server, FLStore reduces average latency by 64.6% and costs by 98.83%, with peak improvements of 98.8% and 99.6%, respectively. FLStore integrates seamlessly with existing FL frameworks with minimal modifications, while also being fault-tolerant and highly scalable.