Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation Under Non-IID Private Data
The paper "Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation Under Non-IID Private Data" addresses a significant challenge in the domain of federated learning (FL) regarding communication overhead and non-IID data distributions across devices. The core contributions are the introduction of Federated Distillation (FD) and Federated Augmentation (FAug), which aim to enhance communication efficiency while maintaining high model accuracy.
Key Innovations
Federated Distillation (FD):
FD is proposed as a communication-efficient alternative to traditional FL by reducing the communication payload. Unlike FL, which requires transmitting large model parameters, FD operates by exchanging smaller-sized logit vectors. This is achieved using an online knowledge distillation approach wherein each device updates its model by comparing its outputs with globally averaged logits from other devices. This methodological shift allows the handling of larger local models without the prohibitive communication cost associated with full model exchanges.
Federated Augmentation (FAug):
To mitigate the performance degradation caused by non-IID data distributions, FAug employs a generative adversarial network (GAN) to augment local datasets, rendering them more IID-like. Each device contributes a few samples of inadequately represented classes to collaboratively train a GAN through a server. The server uses this to oversample and generate data, which assists devices in locally achieving more balanced datasets without significant data exchange, thereby protecting privacy and reducing communication costs.
Empirical Evaluation
The experimental evaluation highlights significant improvements in both communication overhead and model accuracy. When applying FD and FAug to a non-IID MNIST dataset, results indicate a remarkable reduction in communication cost by approximately 26x compared to traditional FL while achieving a test accuracy level of 95-98% of that of FL. The paper emphasizes the robustness of FD and FAug in adapting to non-IID datasets with significant reductions in accuracy loss compared to standalone mobile training.
Implications and Future Directions
The proposed FD and FAug strategies have important implications for on-device ML, particularly in scenarios where bandwidth is constrained or privacy concerns preclude the sharing of raw data. The methods ensure that model training remains efficient and scalable while respecting data privacy constraints.
Further research could explore hybrid approaches that balance the trade-offs between FL and FD, potentially leveraging FL for downlink communications where links are typically more robust. Additionally, integrating differential privacy mechanisms into FAug could further enhance data privacy without compromising model accuracy.
In summary, by focusing on efficient communication and non-IID data handling, this paper makes meaningful strides toward more practical and adaptable distributed ML frameworks. This enhances the feasibility of deploying advanced ML models across various devices with limited communication capabilities.