Federated Learning Debugging via Virtual Model Copies
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Solution Overview
Problem
Debugging poor model performance in federated learning systems is challenging due to the inability to access underlying training datasets, making it difficult to pinpoint local training nodes and models contributing to global model degradation.
Innovation Solution
A device determines global model degradation, selects a trainer node, generates and obtains debugging metrics, and provides an indication of the node responsible for the degradation, allowing for targeted debugging without revealing sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If federated learning is used to train models at local sites without sharing data, then data privacy is protected, but debugging capability is severely limited
Solution Approach 1:
The patent creates virtual copies of local models and training data distributions without actually sharing the real data. By generating synthetic representations (debugging artifacts) that mirror the local training environment, the system enables analysis and debugging while preserving data privacy. The virtual copies allow performance degradation to be traced back to specific local nodes without exposing actual patient data.
Solution Approach 2:
The patent introduces an intermediary debugging system that acts as a mediator between the federated learning nodes and the central coordinator. This intermediary layer collects and processes debugging information from local nodes, enabling performance analysis and root cause identification without requiring direct access to local training data. The intermediary processes information in a way that maintains privacy while enabling effective debugging.
2Measurement precision
If all local training nodes are monitored to identify performance degradation sources, then debugging accuracy is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent segments the debugging process into discrete, manageable components. Instead of monolithic monitoring of all nodes, the system divides debugging into: (1) local model performance evaluation at each node, (2) aggregation of debugging metrics, and (3) root cause analysis. This segmentation allows precise identification of problematic nodes while keeping each individual debugging component simple and manageable.
Solution Approach 2:
The patent applies partial monitoring by focusing debugging resources on specific nodes that show performance degradation rather than continuously monitoring all nodes equally. The system identifies and targets only the necessary subset of nodes for detailed analysis, reducing overall system complexity while maintaining adequate debugging accuracy. This selective approach avoids the excessive resource consumption that would result from full-scale monitoring of all nodes.
3Measurement precision
If debugging metrics are collected from all trainer nodes, then root cause identification is improved, but data transmission and processing overhead increase
Solution Approach 1:
The patent extracts only the essential debugging metrics and information needed for root cause analysis from the full set of node data. Instead of transmitting and processing all raw data from all nodes, the system selectively extracts key performance indicators, model parameters, and debugging artifacts that are sufficient for identification. This extraction approach reduces transmission overhead while maintaining the accuracy needed for effective root cause analysis.
Solution Approach 2:
The patent performs preliminary filtering and processing of debugging information at the local nodes before transmission to the central coordinator. By pre-processing and filtering data locally to identify only the most relevant metrics and anomalies, the system reduces the volume of data that needs to be transmitted and processed centrally. This preliminary action significantly reduces energy consumption and transmission overhead while preserving root cause identification accuracy.
Data Source
AI summary
In one embodiment, a device makes a determination that performance of a global model generated by aggregating local models trained by a plurality of trainer nodes in a federated learning system has experienced a degradation. The device selects, in response to the determination, a particular trainer node from among the plurality of trainer nodes to generate debugging metrics. The device provides an indication that the particular trainer node is a root cause of the degradation.