# Personalized Federated Learning with Hierarchical Two-Branch Aggregation for Few-Shot Scenarios

## TL;DR

This paper introduces pFedH2A, a new method for personalized federated learning that improves performance in few-shot scenarios by using a brain-inspired hierarchical framework.

## Contribution

The novel pFedH2A framework introduces a dual-branch hypernetwork and a relation-aware module for better personalization in few-shot federated learning.

## Key findings

- pFedH2A outperforms existing pFL baselines in few-shot scenarios.
- The dual-branch hypernetwork enables fine-grained personalization by capturing shared and personalized features.
- The relation-aware module supports few-shot classification without rigid assumptions.

## Abstract

Personalized federated learning (pFL) aims to address data heterogeneity by training client-specific models. However, it faces two critical challenges under few-shot conditions. First, existing methods often overlook the hierarchical structure of neural representations, limiting their ability to balance generalization and personalization. Second, recent approaches incorporate representation-level inductive biases that typically rely on rigid assumptions, such as fixed perturbation patterns or compact class clusters, making them vulnerable to distribution shifts in federated environments. To overcome these limitations, we propose pFedH2A, a novel hierarchical framework incorporating brain-inspired mechanisms, tailored for personalized federated learning in few-shot scenarios. First, we design a dual-branch hypernetwork (DHN) that employs two structurally distinct branches to generate aggregation weights. Each branch is biased toward capturing either low-level shared features or high-level personalized representations, enabling fine-grained personalization by mimicking the brain’s division of perceptual and representational processing. Second, we introduce a relation-aware module that learns an adaptive similarity function for each client, supporting few-shot classification by measuring whether a pair of samples belongs to the same class without relying on rigid prototype assumptions. Extensive experiments on public image classification datasets demonstrate that pFedH2A outperforms existing pFL baselines under few-shot scenarios, validating its effectiveness.