Federated Learning Server Using Norm-Clipped Averaging
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Solution Overview
Problem
In federated learning, the quality of collectively trained machine learning models can be compromised by errors in data pre-processing, bugs, or adversarial actions from participating nodes, leading to inconsistent parameter values and training issues.
Innovation Solution
Implementing a collaborative training environment where a server uses norm-clipped averaging and stochastic gradient descent to aggregate parameter updates, with components like consensus determiners, discrepancy determiners, weight determiners, and contribution determiners to manage and weight node contributions, ensuring robust model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple averaging of parameter values from multiple nodes is used, then the aggregation process is simple and fast, but the model quality deteriorates due to inconsistent or adversarial contributions from nodes
Solution Approach 1:
The patent introduces an intermediary verification mechanism where a server acts as a mediator between training nodes. The server receives parameter updates from nodes, verifies their consistency with the global model using a discrepancy determiner, and only accepts updates that meet consistency thresholds. This intermediary verification layer filters out adversarial or erroneous updates while maintaining the overall aggregation process efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where the server provides discrepancy information back to training nodes. The discrepancy determiner calculates how much each node's parameters deviate from the global model, and this feedback is used to adjust node weights in the aggregation process. Nodes with higher discrepancy receive lower weights, creating a feedback loop that continuously improves model quality while maintaining aggregation speed.
2Reliability
If norm-clipped averaging is used to cap parameter values, then the model robustness improves against adversarial actions, but the aggregation complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming the aggregation approach from simple averaging to norm-clipped averaging. Instead of directly averaging parameter values, the system first computes the norm of each node's parameter update, clips these norms to a predefined threshold, and then performs averaging. This parameter transformation ensures that no single node can dominate the aggregation with excessively large updates, thereby improving robustness while adding controlled complexity through the clipping operation.
3Adaptability or versatility
If each node locally computes updates using stochastic gradient descent, then the training flexibility improves, but the consistency of parameter updates deteriorates due to local variations
Solution Approach 1:
The patent applies local quality by allowing each training node to perform local stochastic gradient descent with its own data distribution and hyperparameter settings, maintaining training flexibility. Simultaneously, the server applies global quality control through the discrepancy determiner, which evaluates each node's local updates against the global model state. This dual approach of local freedom with global oversight resolves the contradiction between flexibility and consistency.
Solution Approach 2:
The patent introduces dynamic weight adjustment based on node discrepancy. Instead of using fixed weights for aggregation, the system dynamically adjusts the weight of each node's contribution based on how consistent its updates are with the global model. Nodes that maintain better consistency receive higher weights, while those with large deviations receive lower weights. This dynamic mechanism allows the system to adapt to varying node qualities while maintaining overall parameter consistency.
Data Source
AI summary
Apparatuses, systems, and techniques to collaboratively train one or more machine learning models. Parameter reviewers may be configured to compare sets of machine learning model parameter information in order to generate one or more machine learning models, such as neural networks.