FedRolex: Enhancements in Model-Heterogeneous Federated Learning
The paper "FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction" addresses significant limitations in traditional federated learning (FL) systems that require homogeneity among client and server models. The authors introduce a novel approach named FedRolex to facilitate model-heterogeneous federated learning, enabling the global server model to be larger than any individual client model while mitigating issues such as client drift.
Problem Context
Traditional cross-device federated learning often necessitates identical models across both server and clients, which imposes restrictions on the size of models that clients can handle due to device heterogeneity and computational constraints. This uniformity excludes clients with resource-constrained devices from contributing to FL systems, despite potentially valuable local data, and limits the ability of FL to train more robust, larger models.
FedRolex Methodology and Contributions
FedRolex is a partial training (PT) technique employing a rolling sub-model extraction strategy. The primary elements of FedRolex include:
- Rolling Sub-Model Extraction: This method extracts different sub-models from a global model in a rolling fashion across multiple communication rounds. Each client trains a sub-model tailored to its resource capacity. By iteratively advancing a rolling window for sub-model extraction, the method ensures that all global model parameters are uniformly trained, reducing the risk of client drift due to model heterogeneity.
- Selective Aggregation: FedRolex uses a selective averaging scheme to aggregate the updates received from clients. It averages parameter updates received from any clients that used that parameter, maintaining model synchronization without needing client weighting, which simplifies implementation.
The authors also conduct a theoretical statistical analysis comparing FedRolex to existing methods and demonstrate that FedRolex can train a full global model more efficiently than Federated Dropout by ensuring balanced training of model parameters.
Experimental Evaluation
FedRolex was evaluated against state-of-the-art model-heterogeneous FL methods (e.g., Federated Dropout, HeteroFL) as well as some knowledge distillation (KD) methods (e.g., FedDF). The experiments were conducted across different regimes: small-model with small datasets (CIFAR-10, CIFAR-100) and the large-model with large datasets (Stack Overflow). The evaluation highlights include:
- Enhanced Performance: FedRolex outperformed other PT-based methods consistently across datasets under both low and high heterogeneity scenarios. Particularly, under conditions of high data heterogeneity, FedRolex demonstrated superior performance compared to random sub-model extraction methods like Federated Dropout.
- Improvement Over Model-Homogeneous FL: FedRolex narrows the performance gap between model-heterogeneous and homogeneous settings. This is significant, especially for large-scale datasets, indicating that FedRolex can leverage large client models to enhance global model accuracy efficiently.
- Support for Large Server Models: FedRolex can train server models larger than the largest client model without the need for public data, unlike many KD-based methods, offering improved compatibility with privacy-preserving strategies.
- Robust to Real-World Device Distributions: The algorithm was tested on a modeled real-world distribution of devices, showing enhanced inclusiveness and performance than low-capacity, homogeneous FL.
Implications and Future Directions
FedRolex represents a significant step in making federated learning more flexible and accessible across heterogeneous devices. Its ability to handle model heterogeneity and train large-scale models without compromising model accuracy or privacy is particularly valuable for edge devices and scenarios requiring high-dimensional modeling. Future research could explore convergence analysis and deployment strategies for global models trained under the FedRolex schema to maximize its on-device performance benefits.