# IBM Federated Learning: an Enterprise Framework White Paper V0.1

arXiv: https://arxiv.org/abs/2007.10987 · HTML: https://mlsanity.com/paper/2007.10987/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 30 of 57)

- [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://mlsanity.com/paper/2105.11367/) (2021)
- [Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning](https://mlsanity.com/paper/2108.10241/) (2021)
- [Papaya: Practical, Private, and Scalable Federated Learning](https://mlsanity.com/paper/2111.04877/) (2021)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning](https://mlsanity.com/paper/2204.05562/) (2022)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [FedJAX: Federated learning simulation with JAX](https://mlsanity.com/paper/2108.02117/) (2021)
- [FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data](https://mlsanity.com/paper/2103.03918/) (2021)
- [A Survey on Efficient Federated Learning Methods for Foundation Model Training](https://mlsanity.com/paper/2401.04472/) (2024)
- [FS-Real: Towards Real-World Cross-Device Federated Learning](https://mlsanity.com/paper/2303.13363/) (2023)
- [FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs](https://mlsanity.com/paper/2306.04959/) (2023)
- [FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity](https://mlsanity.com/paper/2206.10546/) (2022)
- [Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity](https://mlsanity.com/paper/2206.02633/) (2022)
- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning](https://mlsanity.com/paper/2112.08524/) (2021)
- [Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory](https://mlsanity.com/paper/2201.02664/) (2022)
- [SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning](https://mlsanity.com/paper/2111.14345/) (2021)
- [Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications](https://mlsanity.com/paper/2304.12012/) (2023)
- [FL-Market: Trading Private Models in Federated Learning](https://mlsanity.com/paper/2106.04384/) (2021)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [Separation of Powers in Federated Learning](https://mlsanity.com/paper/2105.09400/) (2021)
- [FRL: Federated Rank Learning](https://mlsanity.com/paper/2110.04350/) (2021)
- [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://mlsanity.com/paper/2206.09264/) (2022)
- [pfl-research: simulation framework for accelerating research in Private Federated Learning](https://mlsanity.com/paper/2404.06430/) (2024)
- [When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection](https://mlsanity.com/paper/2308.03573/) (2023)
- [FedDebug: Systematic Debugging for Federated Learning Applications](https://mlsanity.com/paper/2301.03553/) (2023)
- [LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning](https://mlsanity.com/paper/2107.12490/) (2021)
- [Federated XGBoost on Sample-Wise Non-IID Data](https://mlsanity.com/paper/2209.01340/) (2022)
- [ModularFed: Leveraging Modularity in Federated Learning Frameworks](https://mlsanity.com/paper/2212.10427/) (2022)

## Abstract (arXiv)

Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. However, solving federated machine learning problems raises issues above and beyond those of centralized machine learning. These issues include setting up communication infrastructure between parties, coordinating the learning process, integrating party results, understanding the characteristics of the training data sets of different participating parties, handling data heterogeneity, and operating with the absence of a verification data set. IBM Federated Learning provides infrastructure and coordination for federated learning. Data scientists can design and run federated learning jobs based on existing, centralized machine learning models and can provide high-level instructions on how to run the federation. The framework applies to both Deep Neural Networks as well as ``traditional'' approaches for the most common machine learning libraries. proj enables data scientists to expand their scope from centralized to federated machine learning, minimizing the learning curve at the outset while also providing the flexibility to deploy to different compute environments and design custom fusion algorithms.