Federated Learning Weight Factorization for Skewed Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional federated machine-learning systems face challenges due to skewed data distribution across clients, leading to poor performance on clients with less common data traits, as they rely on a single global model that may not be fair or effective for all subpopulations.
Innovation Solution
The proposed system employs a Bayesian nonparametric weight factorization approach, splitting the network into server aggregation and client personalization parts, using kernel factorization to reduce data communication and enhance security, and personalizing client models based on local distributions while sharing jointly learned components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single global model is used for federated learning, then the system complexity is reduced, but the model performance and fairness across clients with skewed data distribution deteriorates
Solution Approach 1:
The patent segments the global model into multiple sub-population-specific models. Each client device is assigned to a sub-population group and receives a customized model tailored to that group's data characteristics. This segmentation resolves the contradiction by maintaining manageable system complexity through structured organization while significantly improving model performance and fairness for each sub-population.
Solution Approach 2:
The patent implements local quality by creating models with different characteristics for different sub-populations. Each sub-population model is optimized for its specific data distribution and characteristics, rather than using a uniform global model. This allows the system to maintain overall simplicity while achieving high performance for each local group.
2Measurement precision
If client data is used directly for training, then the model learns from actual data distributions, but data privacy and security are compromised
Solution Approach 1:
The patent introduces an intermediary mechanism where only model parameters and sub-population assignment information are transmitted between clients and the server, never the actual training data. The server acts as a coordinator that aggregates sub-population assignments and distributes appropriate models without accessing client data. This intermediary approach enables accurate learning from local data distributions while maintaining strict data privacy.
3Reliability
If more data is transmitted to the server for centralized training, then the global model can be improved, but communication overhead and energy consumption increase
Solution Approach 1:
The patent extracts only the essential information needed for model distribution—sub-population assignment indicators and model parameters—from the client data. Instead of transmitting raw training data or large amounts of intermediate training results, the system extracts and communicates only the minimal necessary information. This dramatically reduces communication overhead and energy consumption while still enabling effective global model improvement.
4Ease of operation
If a single global model is deployed to all clients, then deployment simplicity is maintained, but fairness and effectiveness for subpopulations with rare data traits deteriorates
Solution Approach 1:
The patent segments the client population into sub-population groups based on data characteristics, and deploys different models to different segments. This segmentation maintains operational simplicity through automated assignment and structured distribution while dramatically improving fairness and effectiveness for underrepresented sub-populations. The system remains easy to operate through automated sub-population detection and model assignment.
Data Source
AI summary
A federated machine-learning system includes a global server and client devices. The server receives updates of weight factor dictionaries and factor strengths vectors from the clients, and generates a globally updated weight factor dictionary and a globally updated factor strengths vector. A client device selects a group of parameters from a global group of parameters, and trains a model using a dataset of the client device and the group of selected parameters. The client device sends to the server a client-updated weight factor dictionary and a client-updated factor strengths vector. The client device receives the globally updated weight factor dictionary and the globally updated factor strengths vector, and retrains the model using the dataset of the client device, the group of parameters selected by the client device, and the globally updated weight factor dictionary and the globally updated factor strengths vector.