Dynamic Aggregation Weights for Federated Learning
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Solution Overview
Problem
Federated learning for neural networks faces challenges in ensuring the quality of global model training due to variations in testing services and data quality across geographically diverse locations, leading to inferior training data for neural networks.
Innovation Solution
Implementing learnable aggregation weights that dynamically adjust based on data distribution and training performance across edge devices, allowing for more accurate weighting of neural network contributions during federated training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed aggregation weights are used for federated learning, then the training process is simple and fast, but the model quality deteriorates due to inferior training data from locations with rudimentary testing services
Solution Approach 1:
The patent implements dynamic aggregation weights that automatically adjust based on data quality assessment. Instead of using fixed weights, the system continuously evaluates the quality of training data from different edge devices and modifies aggregation weights in real-time, allowing high-quality data sources to have greater influence on the global model while reducing the impact of inferior data sources
Solution Approach 2:
The system incorporates a feedback mechanism where the quality of training data is evaluated and this evaluation feeds back into the aggregation weight determination. The quality assessment results from previous training iterations are used to adjust aggregation weights for subsequent iterations, creating a closed-loop system that continuously improves model training quality by learning from past performance
2Measurement precision
If data from all edge devices is aggregated equally, then the training process is efficient, but the model accuracy deteriorates due to inclusion of inferior quality training data
Solution Approach 1:
The patent applies local quality assessment to evaluate the training data from each edge device individually. Instead of treating all data sources uniformly, the system assesses the quality characteristics of data from each location and assigns different aggregation weights accordingly, allowing high-quality data sources to contribute more to the global model while filtering out inferior data
Solution Approach 2:
The system dynamically changes the aggregation weight parameters based on data quality assessment. By adjusting these parameters according to the evaluated quality metrics of training data from different edge devices, the system optimizes model accuracy without significantly impacting training efficiency, as the weight adjustment is integrated into the existing federated learning workflow
Data Source
AI summary
Apparatuses, systems, and techniques to improve federated learning for neural networks. In at least one embodiment, a federated server dynamically selects neural network weights according to one or more learnable aggregation weights indicating a contribution from each of one or more edge devices or clients during federated training according to various characteristics of each edge device or client model and training data.