Federated Learning Hierarchical Bayesian Model
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Solution Overview
Problem
Federated learning technologies face challenges in effectively training machine learning models across client devices with private data, particularly due to communication bottlenecks and data heterogeneity, where traditional methods underperform and require sharing of local data, compromising privacy and efficiency.
Innovation Solution
A hierarchical Bayesian approach is implemented, where a Bayesian hierarchical model links global and local random variables, allowing for the training of machine learning models without sharing client data by using a server to update global and local parameters through block-coordinate optimization, ensuring data privacy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional centralized machine learning is used to train models, then model training efficiency is improved, but data privacy is compromised because all client data must be collected at a central server
Solution Approach 1:
The patent segments the centralized training process into distributed local training at each client device. Each client trains local models independently on their own data without sharing the data itself, thereby maintaining privacy while collectively contributing to global model improvement through parameter sharing.
Solution Approach 2:
The patent introduces a server as an intermediary that facilitates federated learning by distributing global model parameters to clients and collecting updated local parameters back. This intermediary enables coordination without direct data sharing between clients and the server, protecting data privacy while maintaining training efficiency.
2Object-affected harmful factors
If federated learning is used to preserve data privacy, then data privacy is improved, but communication efficiency deteriorates due to multiple rounds of parameter exchange between clients and server
Solution Approach 1:
The patent extracts only the essential model parameters needed for training from each client device and transmits them to the server. By transmitting only parameter updates rather than raw data or complete models, the communication overhead is significantly reduced while maintaining privacy protection.
Solution Approach 2:
The patent implements partial participation in federated learning rounds, where not all clients need to participate in every training iteration. This partial action approach reduces the total communication volume between clients and server while still achieving effective model training through selective client involvement.
3Adaptability or versatility
If personalization is implemented at each client to handle data heterogeneity, then adaptability to local data distributions is improved, but model complexity increases and requires more local data than available
Solution Approach 1:
The patent implements a nested model structure where local personalized models are embedded within a global model framework. Each client maintains a local model that inherits from and is personalized based on the global model parameters, creating a hierarchical nesting that enables adaptation without requiring fully independent complex models at each client.
Solution Approach 2:
The patent applies local quality by allowing each client device to have customized model parameters tailored to its specific data distribution characteristics. Each client's model is optimized for its local data quality and patterns while maintaining consistency with the global model through shared parameter structures, enabling targeted adaptation without uniform complexity across all clients.
Data Source
AI summary
Broadly speaking, embodiments of the present techniques provide a method for training a machine learning, ML, model to update global and local versions of a model. We propose a novel hierarchical Bayesian approach to Federated Learning (FL), where our models reasonably describe the generative process of clients' local data via hierarchical Bayesian modeling: constituting random variables of local models for clients that are governed by a higher-level global variate. Interestingly, the variational inference in our Bayesian model leads to an optimisation problem whose block-coordinate descent solution becomes a distributed algorithm that is separable over clients and allows them not to reveal their own private data at all, thus fully compatible with FL.