Data-Free Knowledge Distillation for Heterogeneous Federated Learning
The paper addresses a significant challenge in federated learning (FL): handling user heterogeneity without relying on a proxy dataset, which is often impractical to obtain. Federated Learning enables collaborative model training without centralized data collection, maintaining user privacy. However, in practice, users' data is typically non-iid and heterogeneous, leading to inefficient model convergence.
To mitigate these issues, the authors propose a novel approach leveraging data-free knowledge distillation (KD). The proposed method, Federated Learning via Generative Distillation (FedGen), introduces a generator model that synthesizes data representations without needing the original data. This synthesized information is then utilized to guide the learning of local models.
Key Contributions
- Data-Free Knowledge Distillation:
- FedGen introduces a generative model to extract and synthesize knowledge from user models, circumventing the necessity of a proxy dataset.
- This generator captures the global data distribution's characteristics by using outputs from the local models as training signals.
- Improved Generalization:
- By distilling aggregated knowledge directly into local models, FedGen manages to enhance model generalization and stabilize learning in heterogeneous settings.
- The paper reports improved generalization performance using fewer communication rounds compared to state-of-the-art methods.
- Compatibility with Privacy Constraints:
- FedGen only requires the exchange of prediction layers or logit outputs, making it adaptable to privacy-preserving applications where sharing full model parameters might be sensitive.
- Empirical Validation:
- Experimental results demonstrate that FedGen outperforms existing baselines under varying degrees of data heterogeneity.
- Strong numerical outcomes indicate its robustness and effectiveness in diverse settings, particularly when conventional approaches like FedAvg encounter performance degradation.
Theoretical Implications
The theoretical analysis connects the role of the generator in inducing an optimal feature space distribution with minimizing domain divergence. By leveraging distribution matching, FedGen aligns user model updates with a more globally consistent feature distribution, enhancing convergence rates and model accuracy.
Future Developments
The paper opens paths for incremental improvements in FL by further optimizing generator design and exploring diverse model architectures. It also sets a foundation for investigating new techniques in privacy-aware FL, where communication efficiency and model security continue to be paramount.
Overall, this paper makes a substantial contribution to federated learning by advancing techniques to manage model performance in heterogeneous settings without additional data resources. The implications of this are broad, affecting applications in various domains, such as healthcare and finance, where privacy and data diversity are crucial considerations.