# Flame: Simplifying Topology Extension in Federated Learning

arXiv: https://arxiv.org/abs/2305.05118 · HTML: https://mlsanity.com/paper/2305.05118/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 45 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 25 of 25)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [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)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://mlsanity.com/paper/2105.11367/) (2021)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [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)
- [HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning](https://mlsanity.com/paper/2002.11343/) (2020)
- [OpenFL: An open-source framework for Federated Learning](https://mlsanity.com/paper/2105.06413/) (2021)
- [FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients](https://mlsanity.com/paper/2201.01601/) (2022)
- [FedJAX: Federated learning simulation with JAX](https://mlsanity.com/paper/2108.02117/) (2021)
- [FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations](https://mlsanity.com/paper/2203.13789/) (2022)
- [Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD](https://mlsanity.com/paper/2010.12998/) (2020)

## Built on by (most cited first, 7 of 7)

- [Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study](https://mlsanity.com/paper/2403.17287/) (2024)
- [LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning](https://mlsanity.com/paper/2405.10968/) (2024)
- [A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks](https://mlsanity.com/paper/2308.05014/) (2023)
- [FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification](https://mlsanity.com/paper/2404.08028/) (2024)
- [Where is the Testbed for my Federated Learning Research?](https://mlsanity.com/paper/2407.14154/) (2024)
- [Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure](https://mlsanity.com/paper/2604.16090/) (2026)
- [Robust Federated Learning Under Real-World Client Churn](https://mlsanity.com/paper/2607.06979/) (2026)

## Abstract (arXiv)

Distributed machine learning approaches, including a broad class of federated learning (FL) techniques, present a number of benefits when deploying machine learning applications over widely distributed infrastructures. The benefits are highly dependent on the details of the underlying machine learning topology, which specifies the functionality executed by the participating nodes, their dependencies and interconnections. Current systems lack the flexibility and extensibility necessary to customize the topology of a machine learning deployment. We present Flame, a new system that provides flexibility of the topology configuration of distributed FL applications around the specifics of a particular deployment context, and is easily extensible to support new FL architectures. Flame achieves this via a new high-level abstraction Topology Abstraction Graphs (TAGs). TAGs decouple the ML application logic from the underlying deployment details, making it possible to specialize the application deployment with reduced development effort. Flame is released as an open source project, and its flexibility and extensibility support a variety of topologies and mechanisms, and can facilitate the development of new FL methodologies.