Federated Learning Aggregation Using Wasserstein Barycenters
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Solution Overview
Problem
Federated learning algorithms face significant performance reduction due to data heterogeneity and noise, especially when edge devices are exposed to only subsets of the global data spectrum, leading to challenges in privacy preservation and model accuracy.
Innovation Solution
Utilize Wasserstein barycenters for creating local representations on edge devices, aggregating them at a central server to generate global representations, and distributing these back to devices for classification, leveraging optimal transport to minimize noise impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning algorithms are used with decentralized models on edge devices, then privacy preservation and computational efficiency are improved, but model accuracy deteriorates due to data heterogeneity and noise
Solution Approach 1:
The patent introduces Wasserstein barycenters as intermediary representations that mediate between local edge device data and the global model. These barycenters serve as compressed, noise-resistant summaries of local data distributions, enabling accurate aggregation without requiring raw data sharing, thus resolving the contradiction between privacy preservation and model accuracy.
Solution Approach 2:
The patent changes the aggregation parameter from traditional arithmetic mean to Wasserstein barycenter computation. This parameter change enables the system to handle noisy and heterogeneous data more effectively, improving model accuracy while maintaining the privacy-preserving decentralized architecture.
2Loss of information
If edge devices are exposed to only subsets of global data (local data), then data privacy is maintained, but model performance deteriorates due to data heterogeneity
Solution Approach 1:
The patent creates local Wasserstein barycenter representations that copy the essential statistical structure of local data distributions. These barycenter copies capture the key characteristics of each device's data subset, enabling the global model to understand local data patterns without accessing the actual data, thus maintaining privacy while improving performance.
Solution Approach 2:
The Wasserstein barycenter approach provides a universal representation that can be computed on any local data distribution regardless of its specific characteristics. This universal method allows devices with highly heterogeneous data to contribute effectively to the global model, bridging the gap between data privacy and model performance.
3Device complexity
If traditional aggregation methods are used in federated learning, then implementation simplicity is maintained, but noise robustness deteriorates in highly noisy data environments
Solution Approach 1:
The patent replaces the mechanical arithmetic mean aggregation with an optimal transport-based Wasserstein barycenter computation. This substitution provides superior noise robustness by computing distributions that minimize transport cost, effectively filtering out noise while maintaining computational feasibility through established OT algorithms.
Data Source
AI summary
Systems and methods are provided for aggregating decentralized machine learning (ML) models in a resilient fashion in the presence of highly noisy data. The systems and methods focus on classification through the use of Wasserstein barycenters (WBs) and enable a geometry-preserving, noise-reducing approach based on optimal transport (OT). These can be used for many applications where there is a large amount of noise and is it desirable to minimize the impact of the noise on a decentralized ML model's performance.