Personalized Federated Learning using Hypernetworks (2103.04628v1)

Abstract: Personalized federated learning is tasked with training machine learning models for multiple clients, each with its own data distribution. The goal is to train personalized models in a collaborative way while accounting for data disparities across clients and reducing communication costs. We propose a novel approach to this problem using hypernetworks, termed pFedHN for personalized Federated HyperNetworks. In this approach, a central hypernetwork model is trained to generate a set of models, one model for each client. This architecture provides effective parameter sharing across clients, while maintaining the capacity to generate unique and diverse personal models. Furthermore, since hypernetwork parameters are never transmitted, this approach decouples the communication cost from the trainable model size. We test pFedHN empirically in several personalized federated learning challenges and find that it outperforms previous methods. Finally, since hypernetworks share information across clients we show that pFedHN can generalize better to new clients whose distributions differ from any client observed during training.

Personalized Federated Learning Using Hypernetworks

The paper "Personalized Federated Learning using Hypernetworks" presents a novel approach to Personalized Federated Learning (PFL) through the introduction of a method called personalized Federated HyperNetworks (pFedHN). This approach addresses the crucial challenge of training personalized machine learning models across various clients, each having its own unique data distribution, while mitigating communication costs and accommodating data heterogeneity.

Overview of pFedHN

The core idea behind pFedHN lies in utilizing hypernetworks: models that generate weights for another model (target network) conditioned on its input. In pFedHN, the hypernetwork serves as a central model that generates unique model parameters for each client. This allows for intelligent parameter sharing among clients while maintaining the flexibility to produce diverse personalized models. By keeping hypernetwork parameters constant and communicating only user-specific model parameters, the approach decouples communication costs from model size, enabling the use of large hypernetworks without inflating communication overhead.

Methodology and Results

The paper demonstrates the effectiveness of pFedHN through experiments on standard datasets such as CIFAR-10, CIFAR-100, and Omniglot. pFedHN shows performance improvements over several existing methods, including FedAvg, Per-FedAvg, LG-FedAvg, and pFedMe. For instance, pFedHN achieves a higher test accuracy in heterogeneous client settings and demonstrates adaptability to new clients with distributions unseen during training.

Additionally, pFedHN handles scenarios where the computational resources of clients differ. This adaptability is achieved by the hypernetwork's ability to produce networks of various sizes, allowing adjustment to the computational limits of the client devices. This feature is particularly advantageous in federated learning environments where clients possess disparate resources.

Theoretical Insights

The paper provides theoretical underpinning for the linear version of pFedHN, showing that in this simplified setting, the method effectively performs a form of principal component analysis (PCA) over client-specific models. This insight reveals the inherent denoising advantage of pFedHN as it can learn to denoise individual models while projecting them onto a shared subspace – a technique useful for inferring new client models based on existing ones.

The paper also discusses generalization bounds, utilizing the framework of multi-task learning. The results indicate that the ability of pFedHN to generalize benefits from the shared hypernetwork, reducing the effective sample complexity related to learning tasks.

Implications and Future Directions

The pFedHN framework hold several implications for the future of federated learning. Practically, it offers a structured model for learning across non-IID data across clients with diverse computational capacities and disparate data distributions. Theoretically, its flexible architecture hints at new paradigms for parameter sharing that mitigate traditional trade-offs between model size and communication overhead.

Looking forward, potential areas for exploration include optimizing hypernetwork architectures to allocate learning capacity between shared and client-specific parameters effectively. Investigating the limits of generalization for unseen clients and refining model robustness further are other avenues for advancement. Additionally, exploring more complex client interactions and adaptive mechanisms for resource-constrained environments may well define the next stage in personalized federated learning methodologies.