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IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, Mark Purcell, Ambrish Rawat, Tran Minh, Naoise Holohan, Supriyo Chakraborty, Shalisha Whitherspoon, Dean Steuer, Laura Wynter, Hifaz Hassan, Sean Laguna, Mikhail Yurochkin, Mayank Agarwal, Ebube Chuba, Annie Abay
TL;DR
Federated learning enables collaborative model training when datasets cannot be centralized because of privacy, confidentiality, regulatory, or volume constraints, while introducing additional coordination and heterogeneity challenges. IBM Federated Learning provides configurable infrastructure and algorithm hooks for extending existing machine-learning workflows across enterprise, edge, cloud, and organizational settings. The framework supports multiple machine-learning paradigms and deployment scenarios, while the paper focuses on single-aggregator operation and optional aggregator-side testing samples.
Problem
Federated learning must support machine learning without centralizing data, while addressing coordination, data heterogeneity, privacy, and enterprise deployment challenges.
Method
IBM Federated Learning provides configurable infrastructure, party and aggregator hooks, and coordinated query, local-training, and fusion functions for federated jobs.
Results
The framework supports extending existing centralized models to federated settings across deep neural networks and traditional approaches, with deployment options spanning mobile, edge, multi-cloud, and organizational environments.
Takeaways & Limitations
Data scientists can use existing machine-learning practices to design federated jobs, while researchers can customize algorithms and protocols for organizational or application-specific needs.
Takeaways & Limitations
The discussion focuses on federated learning with a single aggregator, although alternative decentralized and multiple-aggregator configurations are possible.
Abstract
from arXiv · showhide
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.
1 Introduction
Federated learning addresses cases where training data cannot be centralized because of privacy, regulatory, confidentiality, location, or volume constraints. IBM Federated Learning targets enterprise adoption by coordinating federated workflows while supporting existing machine-learning practices and deployment environments.
- Federated learning motivation: Centralizing training data may be impractical because of privacy, secrecy, regulatory compliance, data location, cost, or data volume.These constraints affect multi-cloud, edge, cross-country, and cross-organization settings.
- Federated learning motivation: Federated learning trains models across multiple parties that retain their datasets instead of sharing them with a central entity.Parties may include enterprise data centers, cloud clusters, devices, or separate organizations.
- Federated learning challenges: Federated learning introduces challenges involving data heterogeneity, coordination, security, privacy inference, fusion, and enterprise deployment.Federated systems lack a common view of the overall training data and may require protections against insights inferred from exchanged messages.
- Enterprise requirements: Enterprise federated learning requires infrastructure for registering parties, distributing models, coordinating training, deploying algorithms, and operating across complex environments.The framework is intended to provide an easy on-ramp for machine-learning professionals while accommodating distributed-systems and cryptography requirements.
- IBM Federated Learning: IBM Federated Learning is proposed as an easy-to-use framework that integrates federated learning into productive enterprise machine-learning workflows.It is positioned for both data scientists and researchers who want to extend existing functionality or try new algorithms and protocols.
- White-paper scope: The white paper presents federated-learning concepts, system architecture, model-training examples, implementation and configuration, and a conclusion.Examples include neural networks and decision trees.
2 Concepts and Terminology
The federated-learning process separates local data access from coordination and fusion: parties compute updates, while an aggregator generates queries and combines responses across rounds. IBM’s framework supports this pattern and allows alternative configurations, although the discussion focuses on a single aggregator.
- Core entities: An FL model M is trained over data split across n parties, with party Pi holding private dataset Di and aggregator A holding no dataset.No party is intended to know another party’s dataset.
- Round workflow: At each round, the aggregator’s query function Q uses the current model or state Mt to generate the next query qt+1.Queries can request local model information or aggregated data-set information.
- Round workflow: Each party applies local training function L to query qt and dataset Di, producing model update ri,t for the aggregator.Queries may initialize local training with model weights or provide candidate features and labels for counting.
- Fusion: The aggregator collects party updates into Rt and applies fusion function F to produce the next model Mt.The resulting model can be distributed through subsequent queries for another training round.
- Termination: Training repeats across rounds until a termination criterion, such as a maximum number of rounds or target condition, produces final global model MG = Mk.The required number of rounds can range from one model merge for Naive Bayes to many rounds for gradient-based algorithms.
- Framework variants: IBM Federated Learning supports neural and non-neural models through configurable party and aggregator components, with fusion potentially more computationally demanding for decision trees.The framework focuses on a single aggregator while acknowledging decentralized and multiple-aggregator variants.
3 Architecture
IBM Federated Learning uses a modular architecture separating communication, data handling, local training, model fusion, and aggregator coordination. Its aggregator and party components support configurable federated jobs across machine-learning libraries and distributed environments.
- The library provides a distributed implementation of federated learning while supporting an easy setup in real environments.
- Its architecture separates communication infrastructure from the federated algorithm and the machine-learning library used for local training.
- The stack includes connection, protocol-handler, data-handler, and federated-training components for coordinating parties and processing local data.
- FusionHandler and LocalTrainingHandler specify aggregator-side fusion and party-side training through standard model APIs that wrap libraries such as Keras and scikit-learn.
- Aggregator stack: The aggregator coordinates training, executes the fusion function, persists process metadata, communicates with parties, and can optionally evaluate global-model performance using testing samples.
- Aggregator stack: The federation registers parties, trains through repeated query-and-update rounds, synchronizes the final global model, optionally evaluates parties, and stops or recovers after errors.
4 Supporting different learning paradigms
IBM Federated Learning separates global fusion from local training so diverse federated algorithms and model types can share a flexible architecture. It also supports privacy mechanisms, robustness algorithms, and common machine-learning libraries.
- Algorithm architecture: Federated algorithms pair an aggregator-side FusionHandler with a party-side LocalTrainingHandler, while accommodating differences in model updates, queries, fusion, and local computation.The framework is designed to support varied learning paradigms rather than a single federation protocol.
- Neural networks: Iterative neural-network training sends global weights and optional hyperparameters to parties, which return locally trained weights for aggregation.FedAvg variants may also include each party’s training-data count in the model update.
- Neural networks: The FLModel module lets users define models in standard libraries while exposing one API to the FusionHandler and LocalTrainingHandler pair.KerasFLModel, for example, wraps initialization, training, and model-update creation for Keras models.
- Machine-learning libraries: The same federated algorithm pair can train models from different libraries, including Keras, PyTorch, TensorFlow, Scikit-learn, and RLlib.Users can extend support by implementing the FLModel interface for additional libraries.
- Decision trees: Federated ID3 grows the decision tree at the aggregator, while parties compute and return local feature-value and class-label counts.The aggregator uses those counts to calculate information gain and split the current node.
- Privacy, robustness, and additional algorithms: The framework includes differential-privacy building blocks, interchangeable cryptosystems, diverse model algorithms, and robustness methods such as Krum, coordinate-wise median fusion, and Zeno.Differential privacy may be applied locally or tracked through an aggregator-side privacy-budget accounting module.
5 Deployment and Configuration
IBM Federated Learning deploys separate aggregator and party stacks, configured through files or an API, and provides commands to register parties, start training, monitor jobs, and collect results.
- Deployment: Deploying IBM Federated Learning requires an aggregator stack and a party stack in each participating environment.The party library is typically installed where local training occurs, following the workflow of other Python packages.
- Configuration: Users configure IBM Federated Learning through external APIs or separate YAML files for the aggregator and each party.The modular design allows components to be swapped without affecting other functionality.
- Configuration: Configuration covers connection, local data loading and preprocessing, federated algorithms, training hyperparameters, models, and protocol handling.Algorithm configuration specifies aggregator-side FusionHandler and party-side LocalTrainingHandler components.
- Job execution: The aggregator launches with its configuration, while party applications launch with individual party configurations and can run across cloud or cluster environments.The aggregator application can be integrated into another application or run independently.
- Job execution: Parties register with the aggregator using REGISTER, and the aggregator waits for a quorum before starting training with TRAIN.Users can observe training through logs and collect results after completion using the provided commands.
- Job execution: The library includes command-line applications and a command set for controlling aggregator and party operations.These commands are documented in Table 1.
6 Conclusion
IBM Federated Learning provides configurable infrastructure for enterprise federated learning across deployment environments and machine-learning paradigms. Its design emphasizes rapid adoption by reusing centralized models, simplifying deployment, and supporting new algorithm development.
- 6 Conclusion: IBM Federated Learning provides hooks for implementing both party and aggregator components of federated learning algorithms.It supports configurable deployment from mobile and edge scenarios to multi-cloud environments and cross-organizational use cases.
- 6 Conclusion: The framework is independent of particular machine-learning libraries and paradigms, supporting deep neural networks and traditional approaches such as decision trees and support vector machines.Federated algorithms can be reused across libraries, including Keras and native PyTorch models.
- 6 Conclusion: Centralized models can often be extended to use data from different parties by editing configuration files rather than rewriting model code.This enables enterprise practitioners to apply federated learning to models already tested in centralized applications.
- 6 Conclusion: Fast start-up is supported by a library of federated algorithms, easier party deployment, and communication options that may not require opening party-side ports.Researchers can also use the existing platform to develop and benchmark new federated learning algorithms.