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

Abstract

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.

arXiv comment17 pages

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• Scikit-learn: Machine Learning in Python
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• Renyi Differential Privacy
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• HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
• BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
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• Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
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Cited by in corpus (15)

• FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
• Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies
• Papaya: Practical, Private, and Scalable Federated Learning
• FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers
• FedJAX: Federated learning simulation with JAX
• FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
• Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
• Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
• Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation
• Model-Agnostic Federated Learning
• DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning
• FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning
• Separation of Powers in Federated Learning
• Management of Resource at the Network Edge for Federated Learning
• Towards General-purpose Infrastructure for Protecting Scientific Data Under Study