Efficient Uncertainty Quantification in Personalized Federated Learning via Bayesian Subnetwork Inference

Core Concepts

FedSI, a novel Bayesian personalized federated learning framework, achieves scalable inference and preserves network uncertainty by leveraging Bayesian methods to perform efficient subnetwork inference.

Abstract

The paper introduces FedSI, a Bayesian personalized federated learning (PFL) framework that addresses the challenges of efficient systematic uncertainty quantification in deep neural networks (DNNs)-based PFL. Key highlights: FedSI implements a client-specific subnetwork inference mechanism, where it selects network parameters with large variance to be inferred through posterior distributions, while fixing the rest as deterministic parameters. This reduces the computational and memory cost while preserving the systematic uncertainties. FedSI decouples the DNN model into representation layers and decision layers. It performs posterior inference over the representation layers and keeps the decision layers fixed during the federated training process. FedSI utilizes a flexible global aggregation strategy that effectively transforms and combines the deterministic representation parameters and stochastic representation parameters, accelerating model convergence. Extensive experiments on three benchmark datasets demonstrate that FedSI outperforms existing Bayesian and non-Bayesian federated learning baselines in heterogeneous federated learning scenarios.

Stats

"While deep neural networks (DNNs) based person- alized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for federated learning (FL) suffer from efficient system- atic uncertainty quantification." "Bayesian DNNs-based PFL algorithms can quantify uncertainties and obtain performance gain on limited data, but they often lead to substantial computational and memory cost or degraded model performance."

Quotes

"FedSI is simple and scalable by lever- aging Bayesian methods to incorporate systematic uncertainties effectively." "FedSI achieves fast and scalable inference while preserving the systematic uncertainties to the fullest extent."

Key Insights Distilled From

FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification

by Hui Chen,Hen... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15657.pdf

Deeper Inquiries

How can the subnetwork selection strategy in FedSI be further improved to better balance the trade-off between computational efficiency and uncertainty preservation

In FedSI, the subnetwork selection strategy plays a crucial role in balancing computational efficiency and uncertainty preservation. To further improve this strategy, one approach could be to incorporate a dynamic subnetwork selection mechanism. Instead of fixing the ratio of subnetwork parameters to the total parameters, the system could adaptively adjust this ratio based on the complexity of the data distribution on each client. By dynamically selecting a subset of parameters with high variance for inference while keeping the rest deterministic, the model can focus on preserving uncertainty where it is most critical. This adaptive approach can help optimize the trade-off between computational efficiency and uncertainty quantification, ensuring that the model adapts effectively to the varying data characteristics across clients.

What are the potential limitations of the Linearized Laplace Approximation used in FedSI, and how can alternative Bayesian inference techniques be explored to enhance the model's performance

The Linearized Laplace Approximation (LLA) used in FedSI has certain limitations that can impact the model's performance. One limitation is the assumption of independence among model parameters, which may not always hold true in complex neural network architectures. This independence assumption can lead to oversimplified posterior approximations, potentially affecting the accuracy of uncertainty quantification. To address this limitation, alternative Bayesian inference techniques such as Hamiltonian Monte Carlo (HMC) or Variational Inference with more flexible distributions can be explored. These methods can capture the dependencies among model parameters more effectively, leading to more accurate posterior approximations and improved uncertainty quantification in the model.

Given the dynamic nature of federated learning systems, how can FedSI be extended to handle the continuous arrival of new clients and efficiently adapt the learned representations to these novel clients

To extend FedSI to handle the continuous arrival of new clients and efficiently adapt learned representations, a few strategies can be implemented. One approach is to incorporate online learning techniques that allow the model to adapt incrementally to new data from novel clients without requiring retraining of the entire model. By updating the common representation layers based on the new client data while keeping the personalized decision layers fixed, the model can quickly adapt to the characteristics of the new clients. Additionally, implementing a transfer learning mechanism that leverages the knowledge learned from previous clients to expedite the learning process for new clients can further enhance the model's adaptability. By fine-tuning the common representation layers based on the new client data and leveraging the existing knowledge, FedSI can efficiently accommodate the continuous influx of novel clients in federated learning systems.