Personalized Federated Learning Gradient Boosted Trees
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Solution Overview
Problem
Federated learning techniques struggle to personalize machine learning models for individual parties, as traditional methods like XGBoost focus on developing a common output without adapting to local data distributions, leading to poor performance when applied to specific user datasets.
Innovation Solution
Implementing a system that interleaves global and local model parameters during training, using gradient descent and Hessian statistics to compute personalized updates, and iteratively refining the global model with local data distributions, allowing for personalized model updates and improved adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional federated learning methods like XGBoost are used to train a common global model, then the model can be efficiently trained across multiple parties without exchanging data, but the model cannot be personalized to fit individual local data distributions, leading to poor performance on specific user datasets
Solution Approach 1:
The patent segments the model training process into global model training and local personalization phases. The global model is trained federatedly across multiple parties, then locally adapted using gradient descent and Hessian statistics computed from local data. This segmentation allows the model to maintain global knowledge while adapting to local distributions without requiring complex end-to-end distributed training.
Solution Approach 2:
The patent applies local quality by computing party-specific updates using local Hessian statistics and gradient information. Each party receives a personalized model update that incorporates their local data distribution characteristics through local gradient descent steps, while the global model structure remains consistent. This enables each party to have a model optimized for their specific data while maintaining overall system coherence.
2Adaptability or versatility
If local model updates are computed using gradient descent and Hessian statistics from local data, then personalized model updates can be generated for each party, but the computational overhead and training complexity increase
Solution Approach 1:
The patent performs preliminary computation of Hessian statistics and gradient information locally before model update generation. By pre-computing these statistics from local data and incorporating them into the federated learning update rule, the system avoids expensive iterative optimization during the federated training phase. This preliminary action enables personalized updates while maintaining training efficiency through closed-form or near-closed-form update solutions.
Solution Approach 2:
The patent uses local copies of the global model as starting points for local personalization. Each party maintains a local copy of the global model parameters and performs local gradient descent steps using their data. This copying approach allows independent local adaptation without requiring continuous communication or coordination during the personalization phase, improving training efficiency while maintaining local adaptability.
Data Source
AI summary
A method, a computer program product, and a system of personalized training a machine learning model using federated learning with gradient boosted trees. The method includes training a global machine learning model using federated learning between a plurality of parties. The method also includes distributing the global machine learning model to each of the parties and receiving personalized model updates from each of the parties. The personalized model updates are generated from updated models boosted locally and produced by each of the parties using their respective local data. The method further includes fusing the personalized model updates to produce a boosted decision tree to update the global machine learning model. The method also includes training global machine learning model, iteratively, in this manner until a stopping criterion is achieved.