Bilevel Federated Learning With Byzantine-Resilient Aggregation
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Solution Overview
Problem
Byzantine attacks pose a significant threat to federated learning systems, leading to poor quality models and slow convergence, particularly in bilevel optimization scenarios where the upper-level solution depends on the lower-level solution, and existing methods fail to effectively defend against such attacks.
Innovation Solution
The BILANTINE algorithm, a Byzantine-resilient federated bilevel optimization method, employs robust aggregation algorithms at each level, adaptively tuning performance based on the number of Byzantine clients, and includes the BYZIN and BYZOUT subroutines to iteratively estimate global models while removing outliers, using Hessian inverse computations for efficient defense.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robust aggregation algorithms are used to defend against Byzantine attacks, then model quality and convergence are improved, but system complexity and computational overhead increase
Solution Approach 1:
The system segments the aggregation process into multiple stages: initial aggregation, outlier detection, and refined aggregation. The aggregator divides clients into different groups based on their model updates and processes them separately, applying different aggregation strategies to different segments. This segmentation allows robust defense against Byzantine attacks while managing system complexity through structured processing.
Solution Approach 2:
The aggregator acts as an intermediary that introduces intermediate representation models during the aggregation process. These intermediate models serve as mediators between client updates and the final global model, allowing the system to detect and filter Byzantine influences without directly processing all raw client updates. This intermediary mechanism improves reliability while keeping the core aggregation logic manageable.
2Reliability
If robust aggregation algorithms are used to defend against Byzantine attacks, then convergence speed is improved, but computational overhead increases
Solution Approach 1:
The system applies partial action by performing outlier detection and robust aggregation only on necessary client updates rather than processing all updates with full robustness algorithms. The aggregator identifies a subset of suspicious updates and applies intensive computational resources only to those cases, while accepting standard aggregation for clearly valid updates. This approach improves convergence speed by avoiding unnecessary computational overhead on benign updates.
Solution Approach 2:
The system dynamically changes aggregation parameters based on detected attack patterns. When Byzantine attacks are detected, the aggregator adjusts weighting parameters, threshold values, and aggregation coefficients to optimize convergence. These parameter changes allow the system to adapt computational effort to the actual threat level, improving convergence speed while managing computational overhead by applying intensive processing only when necessary.
3Reliability
If multiple sets of non-Byzantine clients are identified and used for model generation, then defense against Byzantine attacks is improved, but client communication rounds increase
Solution Approach 1:
The system merges multiple identified sets of non-Byzantine clients into a unified aggregation process. Instead of conducting separate training rounds for each client set, the aggregator combines the model updates from multiple validated client sets in a single communication round. This merging approach maintains strong defense effectiveness by utilizing multiple client sets while reducing communication overhead by processing them together rather than sequentially.
Solution Approach 2:
The system performs preliminary identification and validation of non-Byzantine client sets before the main aggregation process. By pre-screening clients and categorizing them as non-Byzantine in advance, the aggregator can efficiently combine their updates without requiring additional verification rounds during the main training process. This preliminary action reduces communication rounds by resolving client validation beforehand.
Data Source
AI summary
An intermediate global lower-level machine learning model is generated by executing federated learning model training using data from an identified first set of lower-level non-Byzantine client computers which are characterized as being non-Byzantine for the lower-level system. A global lower-level machine learning model is generated by executing the federated learning model training using data from an identified second lower-level set of non-Byzantine client computers which are characterized as being non-Byzantine for the lower-level system. An intermediate global upper-level machine learning model is generated by executing the federated learning model training using data from an identified first upper-level set of non-Byzantine client computers which are characterized as being non-Byzantine for an upper-level system. A global upper-level machine learning model is generated by executing the federated learning model training using data from a second upper-level set of client computers which are characterized as being non-Byzantine for the upper-level system.