Federated Learning Model Diversity via Segmentation
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Solution Overview
Problem
Federated learning systems face inefficiencies and robustness issues due to data heterogeneity, distribution shifts, and communication unreliability, particularly with single global models that can lead to accuracy drops and resource mismanagement across clients with varying hardware capabilities.
Innovation Solution
A novel federated learning framework that stores diverse learnable models on a server, allowing clients to select and combine models based on their resources, with orthogonal regularization and client-side/server-side loss functions to ensure model diversity and robustness, and provides backup solutions for communication failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single global model is used in federated learning, then the system structure is simple, but model accuracy drops and robustness decreases due to data heterogeneity and distribution shifts
Solution Approach 1:
The patent divides the single global model into multiple specialized models, each trained on specific data distributions from different clients. This segmentation allows each model to specialize in handling particular data characteristics, improving overall accuracy while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system dynamically selects and combines multiple models based on the specific data distribution and client requirements rather than using a static single model. This dynamic adaptation allows the system to optimize accuracy for different scenarios while managing complexity through on-demand model selection
2Reliability
If multiple models are maintained for different clients, then model accuracy and robustness improve, but device complexity and resource management become more difficult
Solution Approach 1:
The patent creates a universal model selection framework where a single coordination mechanism can manage multiple specialized models. The system provides multi-functional capabilities including model selection, combination, and replacement through a unified interface, reducing management complexity while maintaining robustness
Solution Approach 2:
The system changes the parameter of model quantity from one to multiple, and introduces parameters for model selection and combination weights. This parameter transformation allows the system to manage complexity through controlled parameter spaces while achieving improved robustness through model diversity
3Adaptability or versatility
If clients with varying hardware capabilities participate, then system adaptability improves, but resource mismanagement and training inefficiency occur
Solution Approach 1:
The patent applies local quality by allowing each client to select and train models according to their specific hardware capabilities and data characteristics. Each client optimizes the model combination for their local conditions, improving adaptability while maintaining training efficiency through personalized resource allocation
Solution Approach 2:
The system allows clients to participate partially by selecting only the models they can effectively train given their hardware constraints. This partial participation approach improves adaptability to varying capabilities while maintaining overall training efficiency through inclusive rather than mandatory participation
4Measurement precision
If data is exchanged between clients, then model training accuracy improves, but data privacy and security risks increase
Solution Approach 1:
The patent extracts only the necessary model parameters and performance metrics from each client's local training, transmitting only these extracted elements to the coordination mechanism rather than raw data. This extraction maintains training accuracy through parameter sharing while eliminating data privacy risks by keeping sensitive data local
Data Source
AI summary
Methods and systems for federated learning in a machine learning environment are disclosed. At least portions of a plurality of server-maintained machine learning models are sent from a server to a plurality of clients, yielding a plurality of local machine learning models. At each client, the plurality of local machine learning models are trained with locally-stored data that is stored locally at that respective client. A respective loss for each of the plurality of local machine learning models is determined, and respective weights for each of the plurality of local machine learning models are updated. The respective updated weights from each client are transferred to the server without transferring the locally-stored data of the clients. At the server, the plurality of server-maintained machine learning models are trained with the updated weights sent from each of the clients.