Distributed Model Training with Collaboration Weights
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Solution Overview
Problem
Collaborative learning methods, such as Federated Averaging, face challenges in handling statistical heterogeneity across decentralized datasets, leading to divergences in weight updates and suboptimal performance due to variations in client distributions, especially in privacy-constrained environments where data cannot be shared directly.
Innovation Solution
Each private dataset learns client weights to combine models' training parameters, allowing for a mixture-based inference model that weighs information from similar datasets more heavily while preserving privacy, by alternating updates of client weights and model parameters in a decentralized manner without a central coordinator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Federated Averaging is used for collaborative learning, then model training can be performed on decentralized datasets without direct data sharing, but the method struggles to handle statistical heterogeneity leading to divergences in weight updates
Solution Approach 1:
The patent applies local quality by allowing each client to maintain its own local model parameters tailored to its specific data distribution, rather than forcing a single global model. Each client's model is optimized for its local data characteristics while still participating in collaborative learning through selective parameter sharing and aggregation, thus adapting to statistical heterogeneity without sacrificing training effectiveness.
Solution Approach 2:
The patent implements dynamics by making the model structure adaptive and flexible. Clients can dynamically select which parameters to share and how to aggregate them based on their local data characteristics. The system allows for dynamic adjustment of model architecture and parameter aggregation strategies to handle varying degrees of statistical heterogeneity across different clients, resolving the contradiction between reliable training and adaptability.
2Productivity
If a single global model is used to aggregate all client data, then collaborative learning can be achieved, but it becomes challenging to provide optimal performance across all clients with different data distributions
Solution Approach 1:
The patent applies segmentation by dividing the global model into multiple local models, each maintained by individual clients. Instead of aggregating all client data into a single global model, the system segments the model parameters and allows each client to have its own specialized model. This segmentation enables each client to achieve optimal performance on its local data while still benefiting from collaborative learning through selective parameter sharing and aggregation.
3Reliability
If multiple models are trained separately on private datasets, then each model can be optimized for its specific data distribution, but the inability to share data prevents leveraging similarities across datasets
Solution Approach 1:
The patent introduces an intermediary mechanism for selective parameter sharing and aggregation. Instead of directly sharing private datasets, clients share selected model parameters through a coordinated aggregation process. This intermediary approach allows information from similar datasets to be leveraged while maintaining data privacy, as the aggregation process selectively combines parameters that capture useful patterns across clients without exposing raw data.
Data Source
AI summary
Model training systems collaborate on model training without revealing respective private data sets. Each private data set learns a set of client weights for a set of computer models that are also learned during training. Inference for a particular private data set is determined as a mixture of the computer model parameters according to the client weights. During training, at each iteration, the client weights are updated in one step based on how well sampled models represent the private data set. In another step, gradients are determined for each sampled model and may be weighed according to the client weight for that model, relatively increasing the gradient contribution of a private data set for model parameters that correspond more highly to that private data set.