Federated Learning Domain Adaptation via Mixture of Experts
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Solution Overview
Problem
Federated learning (FL) lacks effective mechanisms for ensuring data privacy, leading to potential privacy breaches and a trade-off between privacy and prediction accuracy, especially when dealing with diverse data domains.
Innovation Solution
Implementing domain adaptation techniques within a differentially private FL framework, where each user learns a domain-specific expert model and combines it with a general model using a mixture of experts and an attention mechanism to enhance privacy and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If differential privacy is applied to federated learning by adding noise to gradients, then data privacy is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent segments the model training into two distinct components: a general model trained on diverse data from multiple parties with differential privacy, and domain-specific expert models trained on local data without privacy constraints. This segmentation allows each component to optimize for its specific purpose, with the general model providing privacy guarantees and the expert models providing domain-specific accuracy.
Solution Approach 2:
The patent applies different quality standards to different parts of the system. The general model uses differential privacy with noise addition to ensure data privacy, while the domain-specific expert models use local data without noise to maintain high prediction accuracy for their respective domains. The mixture of experts mechanism dynamically selects which model to use based on the input characteristics.
Solution Approach 3:
The patent creates a composite learning system that combines multiple model types (general model and domain-specific expert models) into a unified framework. The mixture of experts mechanism acts as a composite structure that integrates the strengths of both privately-trained and locally-trained models, achieving both privacy preservation and high accuracy.
2Reliability
If gradient noise is increased to strengthen privacy guarantees, then data privacy is improved, but model learning capability deteriorates
Solution Approach 1:
The patent segments the learning process so that the general model learns from privacy-preserving noisy gradients, while domain-specific expert models learn from clean local gradients without noise. This segmentation allows the system to maintain strong privacy guarantees for shared knowledge while preserving full learning capability for domain-specific knowledge.
Solution Approach 2:
The mixture of experts mechanism acts as an intermediary that dynamically routes inputs to appropriate models. When inputs benefit from privacy-preserving general knowledge, the system uses the general model with noisy gradients. When inputs require domain-specific accuracy, the system uses local expert models trained without noise, thus mediating between privacy and learning capability requirements.
3Adaptability or versatility
If federated learning is used to distribute model training across multiple parties, then data centralization is reduced, but coordination complexity increases
Solution Approach 1:
The patent segments the federated learning process into two independent training phases: centralized training of the general model and distributed training of domain-specific expert models. This segmentation simplifies coordination by allowing each phase to operate independently with its own training dynamics, reducing the overall coordination complexity of the distributed system.
Solution Approach 2:
Instead of the traditional approach where all parties contribute to a single shared model, the patent inverts the approach by having parties maintain separate domain-specific models while sharing only a general model. This inversion reduces coordination complexity because parties only need to synchronize the general model updates, not entire model architectures.
Data Source
AI summary
Herein are techniques for domain adaptation of a machine learning (ML) model. These techniques impose differential privacy onto federated learning by the ML model. In an embodiment, each of many client devices receive, from a server, coefficients of a general ML model. For respective new data point(s), each client device operates as follows. Based on the new data point(s), a respective private ML model is trained. Based on the new data point(s), respective gradients are calculated for the coefficients of the general ML model. Random noise is added to the gradients to generate respective noisy gradients. A combined inference may be generated based on: the private ML model, the general ML model, and one of the new data point(s). The noisy gradients are sent to the server. The server adjusts the general ML model based on the noisy gradients from the client devices. This client/server process may be repeated indefinitely.