Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning
The paper introduces FedCGNM, a novel client-side optimizer for Federated Learning that addresses class imbalance by grouping classes based on minimum variance, maintaining separate momentum vectors, and normalizing them to equalize gradient magnitudes between majority and minority classes. FedHOO is proposed as an efficient hyperparameter exploration method using an X-armed bandit approach to optimize time-varying resampling rates, leveraging federated parallelism to evaluate multiple rate comb
Analysis
TL;DR
- The paper introduces FedCGNM, a novel client-side optimizer for Federated Learning that addresses class imbalance by grouping classes based on minimum variance, maintaining separate momentum vectors, and normalizing them to equalize gradient magnitudes between majority and minority classes.
- FedHOO is proposed as an efficient hyperparameter exploration method using an X-armed bandit approach to optimize time-varying resampling rates, leveraging federated parallelism to evaluate multiple rate combinations at linear cost.
- Theoretical convergence analysis is provided that explicitly accounts for time-varying resampling rates, offering rigorous guarantees for the proposed methods in non-IID settings.
- Empirical evaluations on four public long-tailed benchmarks and a proprietary chip-defect dataset demonstrate consistent performance improvements over existing baselines, with additional gains in small-scale federations using FedHOO.
Why It Matters
This research directly tackles one of the most persistent challenges in Federated Learning: handling severe class imbalance without violating privacy constraints or requiring access to global data distributions. By introducing a mechanism that equalizes gradient influence from minority classes, it enables more equitable model performance across diverse client populations, which is critical for real-world deployments in healthcare, industrial defect detection, and personalized services.
Technical Details
- FedCGNM Architecture: The optimizer partitions classes into groups to minimize within-group variance. It maintains a distinct momentum vector for each group, normalizes these vectors to unit length, and sums them to form the final update direction. This process effectively mitigates noise in rare-class gradients and prevents majority classes from dominating the optimization trajectory.
- Theoretical Analysis: The authors provide a convergence proof that incorporates time-varying resampling rates, addressing the dynamic nature of class distribution adjustments during the federated training process.
- FedHOO Algorithm: Designed for small-client regimes, this algorithm utilizes an X-armed bandit framework to select optimal resampling rates. It exploits the inherent parallelism of federated clients to evaluate many combinations of two candidate rates simultaneously, achieving this optimization at linear computational cost relative to the number of clients.
- Evaluation Scope: The methods were tested on four public long-tailed datasets and a proprietary dataset involving chip defects, validating their robustness across different domains and scales.
Industry Insight
- Privacy-Preserving Fairness: Organizations deploying FL in regulated industries (e.g., finance, healthcare) can achieve better fairness metrics for underrepresented groups without centralizing sensitive data, reducing compliance risks associated with data aggregation.
- Scalability in Sparse Networks: The FedHOO approach offers a practical solution for optimizing hyperparameters in federated networks with few active clients, a common scenario in edge computing and IoT deployments where bandwidth and compute resources are limited.
- Adoption of Group-Based Optimizers: The success of class-grouping strategies suggests that future FL frameworks should move beyond simple global averaging or uniform resampling, adopting more sophisticated, group-aware optimization techniques to handle heterogeneous data distributions effectively.
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