Byzantine-Robust Federated Learning via Dynamic Vulnerability Weights
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Solution Overview
Problem
Federated learning systems are vulnerable to Byzantine failures, where inaccurate or malicious gradients from workers can compromise the quality of the global machine learning model, and existing Byzantine-robust methods often require unrealistic assumptions or result in information loss and high computational complexity.
Innovation Solution
The LayerwisE Gradient AggregaTiOn (LEGATO) system dynamically calculates vulnerability weights for each layer of the global model using normalized robustness scores, allowing for layer-specific gradient re-weighting and improved robustness against Byzantine attacks, while maintaining linear computational complexity and generalizing across various attacks and data distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Byzantine-robust methods are used to mitigate dishonest gradients, then the global model quality is improved, but the computational complexity increases significantly
Solution Approach 1:
The patent changes the parameter of gradient aggregation by introducing a vulnerability weight parameter that dynamically adjusts the contribution of each worker's gradient based on its historical reliability. This parameter change allows the system to achieve Byzantine robustness without requiring complex quadratic-time algorithms, instead using a linear-time weighted aggregation approach that maintains computational efficiency while improving model quality.
2Reliability
If previous Byzantine-robust methods are applied, then protection against malicious gradients is achieved, but information loss occurs from rejecting honest gradients
Solution Approach 1:
The patent applies local quality by differentiating the treatment of gradients based on the local reliability of each worker. Instead of uniformly rejecting gradients from suspected Byzantine workers, the system assigns different vulnerability weights to different workers' gradients, allowing honest gradients to be fully utilized while reducing the impact of potentially malicious ones. This localized differentiation prevents information loss from honest gradients.
Solution Approach 2:
The vulnerability weight is dynamically updated based on the historical performance and consistency of each worker's gradients. Workers who consistently provide high-quality gradients receive lower vulnerability weights (higher trust), while those with erratic or suspicious patterns receive higher vulnerability weights (lower trust). This dynamic adaptation allows the system to adapt to changing conditions and avoid permanently rejecting honest workers.
3Productivity
If traditional gradient aggregation is used, then computational efficiency is maintained, but the system becomes vulnerable to Byzantine failures
Solution Approach 1:
The patent introduces an intermediary mechanism in the form of the vulnerability weight, which mediates between the raw gradients from workers and the final aggregated gradient. This intermediary layer filters and adjusts the influence of each worker's gradient based on its reliability, providing Byzantine robustness without sacrificing computational efficiency. The vulnerability weight acts as a protective intermediary that maintains the simplicity of the aggregation process while adding robustness.
Data Source
AI summary
A federated learning method comprises creating a log of previously provided gradients from a plurality of workers, receiving updated gradients from the plurality of workers, calculating a vulnerability weight for each layer of a global machine learning model using the updated gradients, calculating an aggregated gradient using the vulnerability weight and the updated gradients, and updating the global machine learning model using the aggregated gradient. Some embodiments may also determine whether a Byzantine attack is occurring based upon the calculated aggregated gradient. An apparatus and computer program product may be used to implement the method.