# FedLess: Secure and Scalable Federated Learning Using Serverless Computing arXiv: https://arxiv.org/abs/2111.03396 ยท HTML: https://mlsanity.com/paper/2111.03396/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 74 references that are in the map. - First posted on arXiv 5 Nov 2021. ## Builds on (most cited first, 21 of 21) - [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016) - [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019) - [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) - [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019) - [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018) - [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020) - [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020) - [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017) - [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018) - [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015) - [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020) - [A General Approach to Adding Differential Privacy to Iterative Training Procedures](https://mlsanity.com/paper/1812.06210/) (2018) - [Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication](https://mlsanity.com/paper/2102.04737/) (2021) - [An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies](https://mlsanity.com/paper/2004.04676/) (2020) - [Towards Demystifying Serverless Machine Learning Training](https://mlsanity.com/paper/2105.07806/) (2021) - [Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning](https://mlsanity.com/paper/2011.12623/) (2020) - [Architecture-Specific Performance Optimization of Compute-Intensive FaaS Functions](https://mlsanity.com/paper/2107.10008/) (2021) - [Towards Demystifying Intra-Function Parallelism in Serverless Computing](https://mlsanity.com/paper/2110.12090/) (2021) - [Status of Serverless Computing and Function-as-a-Service(FaaS) in Industry and Research](https://mlsanity.com/paper/1708.08028/) (2017) - [Function Delivery Network: Extending Serverless Computing for Heterogeneous Platforms](https://mlsanity.com/paper/2102.02330/) (2021) ## Built on by (most cited first, 13 of 13) - [SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing](https://mlsanity.com/paper/2202.05800/) (2022) - [Towards cost-effective and resource-aware aggregation at Edge for Federated Learning](https://mlsanity.com/paper/2204.07767/) (2022) - [Exploring the Impact of Serverless Computing on Peer To Peer Training Machine Learning](https://mlsanity.com/paper/2309.14139/) (2023) - [SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture](https://mlsanity.com/paper/2309.14148/) (2023) - [Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning](https://mlsanity.com/paper/2409.16495/) (2024) - [FedLesScan: Mitigating Stragglers in Serverless Federated Learning](https://mlsanity.com/paper/2211.05739/) (2022) - [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) - [Architecting Peer-to-Peer Serverless Distributed Machine Learning Training for Improved Fault Tolerance](https://mlsanity.com/paper/2302.13995/) (2023) - [Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training](https://mlsanity.com/paper/2303.05330/) (2023) - [Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments](https://mlsanity.com/paper/2404.14033/) (2024) - [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) The traditional cloud-centric approach for Deep Learning (DL) requires training data to be collected and processed at a central server which is often challenging in privacy-sensitive domains like healthcare. Towards this, a new learning paradigm called Federated Learning (FL) has been proposed that brings the potential of DL to these domains while addressing privacy and data ownership issues. FL enables remote clients to learn a shared ML model while keeping the data local. However, conventional FL systems face several challenges such as scalability, complex infrastructure management, and wasted compute and incurred costs due to idle clients. These challenges of FL systems closely align with the core problems that serverless computing and Function-as-a-Service (FaaS) platforms aim to solve. These include rapid scalability, no infrastructure management, automatic scaling to zero for idle clients, and a pay-per-use billing model. To this end, we present a novel system and framework for serverless FL, called FedLess. Our system supports multiple commercial and self-hosted FaaS providers and can be deployed in the cloud, on-premise in institutional data centers, and on edge devices. To the best of our knowledge, we are the first to enable FL across a large fabric of heterogeneous FaaS providers while providing important features like security and Differential Privacy. We demonstrate with comprehensive experiments that the successful training of DNNs for different tasks across up to 200 client functions and more is easily possible using our system. Furthermore, we demonstrate the practical viability of our methodology by comparing it against a traditional FL system and show that it can be cheaper and more resource-efficient.