Decentralized Federated Learning via Distributed Ledger Clustering
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Solution Overview
Problem
Existing federated learning approaches rely on a trusted central party, making them unsuitable for complex scenarios like healthcare and financial services, where data from multiple stakeholders with subtly different properties is involved, leading to poor generalization of machine learning algorithms.
Innovation Solution
A decentralized collective learning platform using a distributed ledger, secondary distributed ledgers, and homomorphic encryption to enable worker nodes to train models without sharing data, clustering nodes based on generalization performance, and incentivizing participation through reputation mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a trusted central party is used to aggregate information in federated learning, then model training can be performed without sharing raw data, but control of the model is concentrated in a single party making it unsuitable for complex scenarios like healthcare and financial services
Solution Approach 1:
The system segments the centralized control function into distributed components by introducing multiple aggregator nodes that collectively perform model aggregation. Each aggregator handles a subset of updates, distributing control across multiple independent parties rather than concentrating it in a single central server. This enables multi-stakeholder scenarios while maintaining data privacy through the federated learning architecture.
Solution Approach 2:
The system merges the functions of multiple independent aggregators into a coordinated ensemble that collectively performs model aggregation. By combining the capabilities of multiple distributed aggregators with verifiable random function-based selection, the system achieves both decentralization (suitability for multi-stakeholder scenarios) and reliability (data privacy protection through cryptographic verification).
2Adaptability or versatility
If machine learning algorithms are trained on heterogeneous data from multiple parties, then collective learning can be achieved, but the algorithms generalize poorly due to data fragility and brittleness
Solution Approach 1:
The system performs preliminary actions by selecting aggregators and determining update incorporation before the actual model training process using verifiable random functions. This pre-selection mechanism ensures that only verified, compatible updates from heterogeneous data sources are incorporated, preventing data fragility from degrading model generalization while maintaining collective learning capability across multiple parties.
Solution Approach 2:
The system implements feedback mechanisms through verifiable random functions that verify the authenticity and compatibility of model updates before aggregation. This feedback loop ensures that heterogeneous data contributions are validated for compatibility, maintaining model generalization performance while enabling collective learning across diverse data sources with different properties.
3Productivity
If existing federated learning approaches are used, then model updates can be aggregated from multiple sources, but the system requires a central server that creates single points of failure and control
Solution Approach 1:
The system introduces verifiable random functions as cryptographic intermediaries that mediate between model updates and the aggregation process. These intermediaries enable automated, trustless verification of update authenticity without requiring a central server, maintaining aggregation efficiency while reducing architectural complexity and eliminating single points of control or failure.
Solution Approach 2:
The system enables self-service through automated verifiable random function-based selection and verification mechanisms that operate without central coordination. Each participant independently verifies and contributes updates, with the system automatically selecting and aggregating compatible updates, thereby maintaining productivity while simplifying the architectural complexity by removing the need for a central server.
Data Source
AI summary
Disclosed is a distributed computer system that includes a plurality of worker nodes that are coupled together via a data communication network to exchange data therebetween, wherein collective learning of the worker nodes is managed within the distributed computer system. The distributed computer system comprises a data processing arrangement operable to cluster the plurality of worker nodes into one or more clusters, wherein worker nodes of a given cluster train a computing model by employing a respective secondary distributed ledger. The collective learning from the plurality of worker nodes is coordinated using the distributed ledger arrangement.