Federated Learning Uncertainty Routing With Local and Global Models
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Solution Overview
Problem
Current federated learning systems face challenges in accurately quantifying uncertainty, particularly in distinguishing between aleatoric and epistemic uncertainties, and differentiating between local and global in-and out-of-distribution data, which compromises model robustness and reliability.
Innovation Solution
A framework called FedPN uses Dirichlet-based Natural Posterior Networks (NatPN) to estimate aleatoric and epistemic uncertainties, with a modified loss function that maximizes embedding likelihood and prevents Bayesian loss impact, allowing switching between local and global models based on uncertainty scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an ensemble of K global models is used for federated uncertainty quantification, then uncertainty quantification accuracy is improved, but computational cost increases K times compared to classical FedAvg
Solution Approach 1:
The system segments the uncertainty quantification task by maintaining separate local models at each client and a global model at the server. Each client computes uncertainty using its local model, avoiding the need to maintain multiple global models. This segmentation reduces computational cost while preserving uncertainty quantification capability through distributed computation.
Solution Approach 2:
Instead of using an ensemble of K global models, the system uses a single global model combined with local models at each client. The local models serve as copies adapted to client-specific data distributions, enabling uncertainty quantification without requiring multiple global model copies, thus reducing computational overhead.
2Measurement precision
If Markov Chain Monte Carlo (MCMC) is used to obtain samples from posterior distribution, then uncertainty estimation is improved, but computational complexity makes it practically infeasible
Solution Approach 1:
The system replaces the complex mechanical process of MCMC sampling with a direct analytical approach using the Dirichlet distribution. By substituting the iterative sampling mechanism with a closed-form posterior calculation, the system achieves uncertainty estimation without the computational complexity of MCMC, making it feasible for practical federated learning applications.
3Measurement precision
If local models are used for personalization, then prediction accuracy for local data is improved, but reliability of global model decreases
Solution Approach 1:
The system merges local and global models into a unified architecture where both models coexist and serve different purposes. The local model provides personalization for client-specific data while the global model maintains overall reliability and handles out-of-distribution cases. This merging allows the system to leverage the strengths of both approaches simultaneously.
Solution Approach 2:
The global model serves multiple functions: it provides a baseline for all clients, handles out-of-distribution data for any client, and serves as a reference for uncertainty calibration. This multi-functionality ensures that global model reliability is maintained while local models can still provide personalization benefits when needed.
4Reliability
If uncertainty quantification is implemented in federated learning, then model robustness is improved, but system complexity increases
Solution Approach 1:
The system changes the parameters of the Dirichlet distribution dynamically based on local data characteristics at each client. By adjusting these parameters according to local conditions, the system achieves robust uncertainty quantification without requiring complex system-wide coordination, thus improving robustness while controlling system complexity through localized parameter adaptation.
Data Source
AI summary
A method and system for uncertainty quantification approach for federated learning that enables the distinction between aleatoric and epistemic uncertainties, as well as between local and global in-and out-of-distribution data. The method and system offer permit selecting the appropriate model to predict on a given input based on these uncertainty estimations. This comprehensive framework contributes to enhancing the robustness and reliability of federated learning models in real-world applications, effectively addressing the challenges that arise due to the heterogeneity and diverse nature of data distributions.