Federated Modular Networks for Heterogeneous Client Personalization
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Solution Overview
Problem
Existing personalized federated learning approaches fail to effectively address joint distribution heterogeneity among clients, leading to sub-optimal solutions due to assumptions of identical marginal or conditional distributions, which results in poor model generalization and increased communication costs.
Innovation Solution
The Federated Modular Networks (FedMN) approach models the joint distribution of each client and uses a routing hypernetwork to select a combination of modular blocks from a shared pool, allowing clients to assemble personalized models and reduce communication burden by uploading only necessary model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing personalized federated learning approaches are used, then model training can be performed under privacy constraints, but model generalization performance deteriorates when joint distribution among clients diverges
Solution Approach 1:
The patent segments the model into multiple independent modules that can be selectively activated. Instead of using a single homogeneous model for all clients, the system divides the model into modular components that can be independently selected based on client-specific data distribution characteristics, thereby improving generalization performance under heterogeneity.
Solution Approach 2:
The patent introduces dynamic model personalization where the model architecture adapts to each client's data distribution. The system dynamically selects and activates specific model modules for each client based on their local data characteristics, making the model flexible and adaptable rather than static and uniform.
2Device complexity
If homogeneous model structures are used across all clients, then system complexity is reduced, but communication costs increase due to uploading complete model parameters
Solution Approach 1:
The patent extracts and transmits only the necessary model parameters for each client rather than complete model parameters. By identifying and selecting only the relevant module parameters needed for each client's local training, the system significantly reduces communication overhead while maintaining model effectiveness.
Solution Approach 2:
The patent implements local quality by allowing each client to have a customized model configuration tailored to their specific data distribution. Instead of enforcing uniform model structures across all clients, the system enables each client to activate only the model modules that are most suitable for their local data characteristics.
3Measurement precision
If complete model parameters are uploaded for each client, then model personalization accuracy is improved, but communication burden increases
Solution Approach 1:
The patent extracts and transmits only the necessary model parameters for each client rather than complete model parameters. By identifying and selecting only the relevant module parameters needed for each client's local training, the system significantly reduces communication overhead while maintaining model effectiveness.
Solution Approach 2:
The patent applies partial action by transmitting a subset of model parameters rather than the complete set. The system selectively uploads only the parameters corresponding to the activated modules for each client, which is a partial transmission that suffices for achieving accurate personalization without the excess of transmitting all parameters.
Data Source
AI summary
A computer-implemented method for personalizing heterogeneous clients is provided. The method includes initializing a federated modular network including a plurality of clients communicating with a server, maintaining, within the server, a heterogenous module pool having sub-blocks and a routing hypernetwork, partitioning the plurality of clients by modeling a joint distribution of each client into clusters, enabling each client to make a decision in each update to assemble a personalized model by selecting a combination of sub-blocks from the heterogenous module pool, and generating, by the routing hypernetwork, the decision for each client.