Federated Learning Model Customization via Collaboration Coefficients
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Solution Overview
Problem
Existing horizontal federated learning approaches struggle to customize models for clients with non-IID local datasets while ensuring data privacy, as they often rely on centralized models that may not fit individual datasets well and compromise privacy by sharing data.
Innovation Solution
The method employs collaboration coefficients to perform weighted aggregation of local model parameters, allowing clients to update their models based on similarity, thereby enabling customized models for each client without sharing private data, using techniques like cosine similarity and clustering for collaborative learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single centralized model is learned for all clients using federated averaging or federated proximal, then data privacy is maintained, but the model cannot be customized for each client's non-IID local dataset
Solution Approach 1:
The patent segments the learning process into two distinct phases: a centralized pre-training phase that creates a base model, and a federated fine-tuning phase that customizes the model for each client. This segmentation allows the system to benefit from both centralized training (for data efficiency and model quality) and decentralized fine-tuning (for customization and privacy preservation), resolving the contradiction between model adaptability and local dataset fit.
Solution Approach 2:
The patent performs preliminary centralized pre-training to create a high-quality base model before distributing it to clients for fine-tuning. This preliminary action ensures that the base model already captures important patterns from diverse data, providing a strong foundation that accelerates local fine-tuning and improves final model performance on non-IID datasets.
2Manufacturing precision
If local datasets are centralized for traditional machine learning, then a single model can be trained effectively, but data privacy is compromised due to regulations like GDPR
Solution Approach 1:
The patent introduces a centralized server as an intermediary that facilitates model training without direct access to client data. The server coordinates the pre-training phase using aggregated data from multiple clients and then manages the distribution of the base model for federated fine-tuning. This intermediary role enables effective model training while maintaining data privacy, as the server never directly accesses or stores individual client datasets.
Solution Approach 2:
The patent creates a copy of the training process that operates on model parameters rather than raw data. Instead of centralizing sensitive datasets, the system centralizes the copying and aggregation of model updates during pre-training, then distributes the base model copy to clients for local fine-tuning. This copying approach maintains training quality while eliminating data privacy risks associated with centralizing original datasets.
3Object-affected harmful factors
If federated learning is performed without pre-training, then data privacy is maintained, but the model performance on non-IID datasets is suboptimal
Solution Approach 1:
The patent segments the learning process into centralized pre-training and decentralized federated fine-tuning phases. This segmentation allows the system to leverage centralized computing resources for initial model development (improving performance) while maintaining data privacy during the federated fine-tuning phase. The two-phase approach resolves the contradiction by applying different strategies at different stages of the learning process.
Solution Approach 2:
The patent applies different learning strategies to different phases of the process: centralized pre-training for global pattern recognition and federated fine-tuning for local adaptation. This local quality approach optimizes each phase for its specific purpose while maintaining overall data privacy, achieving both high model performance and privacy protection.
4Object-affected harmful factors
If existing federated learning approaches are used, then data privacy is preserved, but the approaches require strict assumptions and simple convex models that limit applicability
Solution Approach 1:
The patent employs a dynamic two-phase learning approach that adapts to different model types and data distributions. The centralized pre-training phase can handle complex non-convex models and diverse data types, while the federated fine-tuning phase adapts to each client's specific non-IID data characteristics. This dynamic approach removes the need for strict convexity assumptions and extends federated learning applicability to complex deep learning models.
Solution Approach 2:
The patent changes the learning parameters and objectives between phases: centralized pre-training uses standard loss minimization on aggregated data, while federated fine-tuning uses local adaptation with regularization toward the base model. This parameter change strategy enables the system to handle complex non-convex models and diverse data types without requiring strict convexity assumptions, significantly expanding model type flexibility.
Data Source
AI summary
Methods and systems for horizontal federated learning are described. A plurality of sets of local model parameters is obtained. Each set of local model parameters was learned at a respective client. For each given set of local model parameters, collaboration coefficients are computed, representing a similarity between the given set of local model parameters and each other set of local model parameters. Updating of the sets of local model parameters is performed, to obtain sets of updated local model parameters. Each given set of local model parameters is updated using a weighted aggregation of the other sets of local model parameters, where the weighted aggregation is computed using the collaboration coefficients. The sets of updated local model parameters are provided to each respective client.