mlsanityData to 14 Oct 2026

ModularFed: Leveraging Modularity in Federated Learning Frameworks

arXiv 2212.10427 ↗First posted 31 Oct 2022cs.DC cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms. However, the standards of the available frameworks can no longer sustain the rapid advancement and hinder the integration of FL solutions, which can be prominent in advancing the field. In this paper, we propose ModularFed, a research-focused framework that addresses the complexity of FL implementations and the lack of adaptability and extendability in the available frameworks. We provide a comprehensive architecture that assists FL approaches through well-defined protocols to cover three dominant FL paradigms: adaptable workflow, datasets distribution, and third-party application support. Within this architecture, protocols are blueprints that strictly define the framework's components' design, contribute to its flexibility, and strengthen its infrastructure. Further, our protocols aim to enable modularity in FL, supporting third-party plug-and-play architecture and dynamic simulators coupled with major built-in data distributors in the field. Additionally, the framework support wrapping multiple approaches in a single environment to enable consistent replication of FL issues such as clients' deficiency, data distribution, and network latency, which entails a fair comparison of techniques outlying FL technologies. In our evaluation, we examine the applicability of our framework addressing three major FL domains, including statistical distribution and modular-based approaches for resource monitoring and client selection.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 46 references that are in the map. First posted on arXiv 31 Oct 2022.

Builds on

20 of 46 references.

References · 20

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020Federated Learning with Matched Averagingcited by 564
  3. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  4. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  5. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  6. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  7. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  8. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  9. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  10. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  11. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 175
  12. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 160
8 more
  1. 2018Blockchained On-Device Federated Learningcited by 106
  2. 2020Robust Federated Learning: The Case of Affine Distribution Shiftscited by 102
  3. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 76
  4. 2020Data Poisoning Attacks on Federated Machine Learningcited by 62
  5. 2020Cost-Effective Federated Learning Designcited by 62
  6. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  7. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  8. 2020Dynamic Clustering in Federated Learningcited by 27

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2023CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environmentcited by 3
  2. 2024CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authenticationcited by 2
  3. 2024Trust Driven On-Demand Scheme for Client Deployment in Federated Learningcited by 1
  4. 2024On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learningcited by 1