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

Heiko Ludwig, Nathalie Baracaldo+22 more|arXiv (Cornell University)|Jul 22, 2020

Privacy-Preserving Technologies in Data25 references111 citations

TL;DR

IBM Federated Learning presents an enterprise-ready framework to perform federated learning, enabling training across distributed data sources with modular architecture, supporting multiple ML libraries and privacy-preserving options.

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.

Motivation & Objective

• Motivate and define federated learning in enterprise settings where data cannot be centralized due to privacy, regulatory, or logistical constraints.
• Propose an easy-to-use, extensible framework that integrates federated learning with existing ML workflows and libraries.
• Provide an architecture that supports diverse compute environments and data heterogeneity while enabling secure collaboration.
• Facilitate rapid deployment of federated learning in production by simplifying onboarding for ML professionals and IT operations.

Proposed method

• Present a formal FL model with an aggregator A and parties Pi, detailing the query L and fusion F processes that update a global model MG from local updates.
• Describe a layered architecture for IBM Federated Learning, including Connection, Protocol Handler, Data Handler, FusionHandler, LocalTrainingHandler, and FLModel adapters.
• Explain how neural networks and decision trees can be trained in a federated setting using IterAvg/FedAvg and ID3-based approaches, respectively.
• Support multiple ML libraries via FLModel wrappers (e.g., Keras, PyTorch, TensorFlow, Scikit-learn) to enable cross-library interoperability.
• Discuss privacy-preserving extensions including differential privacy and secure multi-party computation, with a flexible API to incorporate cryptographic techniques.

Experimental results

Research questions

• RQ1 How can federated learning be effectively implemented in enterprise environments with data partitioning across clouds, on-premises data centers, and edge devices?
• RQ2 What architectural patterns and components are required to integrate FL into existing ML workflows with minimal learning curve for data scientists?
• RQ3 How can common FL algorithms (e.g., FedAvg, Krum, Zeno) be realized within a modular IBM Federated Learning framework?
• RQ4 What privacy and security mechanisms (differential privacy, SMC) can be integrated into the framework without prohibitive communication or computation overhead?
• RQ5 How can different ML libraries be supported through a common API to train diverse model types in federated settings?

Key findings

• Define a modular IBM Federated Learning architecture with clear separation of concerns between aggregator and party stacks.
• Show how neural networks and decision trees can be incorporated via dedicated FusionHandler and LocalTrainingHandler pairs.
• Demonstrate integration with multiple ML libraries through a common FLModel interface, enabling reuse across models like Keras and Scikit-learn.
• Outline support for privacy-preserving techniques such as differential privacy and secure multi-party computation within the framework.
• Identify practical considerations for deployment in enterprise and multi-cloud environments, including network, security, and operational resilience.