mlsanityData to 14 Oct 2026

Flame: Simplifying Topology Extension in Federated Learning

arXiv 2305.05118 ↗First posted 9 May 2023cs.LG cs.DCcited by 7

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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.

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

25 of 45 references.

References · 25

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2020Adaptive Federated Optimizationcited by 1,046
  5. 2019Towards Federated Learning at Scale: System Designcited by 832
  6. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  7. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  8. 2019Fair Resource Allocation in Federated Learningcited by 394
  9. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  10. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  11. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  12. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
13 more
  1. 2015MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systemscited by 195
  2. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
  3. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  4. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  5. 2018A generic framework for privacy preserving deep learningcited by 103
  6. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 90
  7. 2019Peer-to-peer Federated Learning on Graphscited by 85
  8. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  9. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  10. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  11. 2021FedJAX: Federated learning simulation with JAXcited by 32
  12. 2022FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulationscited by 27
  13. 2020Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDcited by 26

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 0; 2026: 2.

7 papers.

  1. 2024Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Studycited by 10
  2. 2024LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learningcited by 2
  3. 2023A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworkscited by 0
  4. 2024FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classificationcited by 0
  5. 2024Where is the Testbed for my Federated Learning Research?cited by 0
  6. 2026Robust Synchronisation for Federated Learning in The Face of Correlated Device Failurecited by 0
  7. 2026Robust Federated Learning Under Real-World Client Churncited by 0