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Hierarchical Knowledge Structuring for Effective Federated Learning in Heterogeneous Environments

Wai Fong Tam, Qilei Li, Ahmed M. Abdelmonie

TL;DR

This paper addresses the tension between personalization and global generalization in Federated Learning under non-IID data and device heterogeneity. It introduces Hierarchical Knowledge Structuring (HKS), which builds a multi-granularity codebook of logits via unsupervised bottom-up clustering at the server, enabling per-sample, per-class, and global knowledge sharing without reliance on labels. Local clients incorporate knowledge distillation from the hierarchical logits into their training, improving convergence and robustness across heterogeneous settings. Across FashionMNIST with heterogeneous ResNet backbones, HKS yields superior personalized accuracy while maintaining competitive global performance compared to FedAvg, FedDistill, and FedCache, particularly under higher data heterogeneity. The approach offers a privacy-preserving, scalable framework for cross-client knowledge exchange in FL.

Abstract

Federated learning enables collaborative model training across distributed entities while maintaining individual data privacy. A key challenge in federated learning is balancing the personalization of models for local clients with generalization for the global model. Recent efforts leverage logit-based knowledge aggregation and distillation to overcome these issues. However, due to the non-IID nature of data across diverse clients and the imbalance in the client's data distribution, directly aggregating the logits often produces biased knowledge that fails to apply to individual clients and obstructs the convergence of local training. To solve this issue, we propose a Hierarchical Knowledge Structuring (HKS) framework that formulates sample logits into a multi-granularity codebook to represent logits from personalized per-sample insights to globalized per-class knowledge. The unsupervised bottom-up clustering method is leveraged to enable the global server to provide multi-granularity responses to local clients. These responses allow local training to integrate supervised learning objectives with global generalization constraints, which results in more robust representations and improved knowledge sharing in subsequent training rounds. The proposed framework's effectiveness is validated across various benchmarks and model architectures.

Hierarchical Knowledge Structuring for Effective Federated Learning in Heterogeneous Environments

TL;DR

This paper addresses the tension between personalization and global generalization in Federated Learning under non-IID data and device heterogeneity. It introduces Hierarchical Knowledge Structuring (HKS), which builds a multi-granularity codebook of logits via unsupervised bottom-up clustering at the server, enabling per-sample, per-class, and global knowledge sharing without reliance on labels. Local clients incorporate knowledge distillation from the hierarchical logits into their training, improving convergence and robustness across heterogeneous settings. Across FashionMNIST with heterogeneous ResNet backbones, HKS yields superior personalized accuracy while maintaining competitive global performance compared to FedAvg, FedDistill, and FedCache, particularly under higher data heterogeneity. The approach offers a privacy-preserving, scalable framework for cross-client knowledge exchange in FL.

Abstract

Federated learning enables collaborative model training across distributed entities while maintaining individual data privacy. A key challenge in federated learning is balancing the personalization of models for local clients with generalization for the global model. Recent efforts leverage logit-based knowledge aggregation and distillation to overcome these issues. However, due to the non-IID nature of data across diverse clients and the imbalance in the client's data distribution, directly aggregating the logits often produces biased knowledge that fails to apply to individual clients and obstructs the convergence of local training. To solve this issue, we propose a Hierarchical Knowledge Structuring (HKS) framework that formulates sample logits into a multi-granularity codebook to represent logits from personalized per-sample insights to globalized per-class knowledge. The unsupervised bottom-up clustering method is leveraged to enable the global server to provide multi-granularity responses to local clients. These responses allow local training to integrate supervised learning objectives with global generalization constraints, which results in more robust representations and improved knowledge sharing in subsequent training rounds. The proposed framework's effectiveness is validated across various benchmarks and model architectures.

Paper Structure

This paper contains 21 sections, 5 equations, 3 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Federated Learning
  4. Personalized Federated Learning (PFL)
  5. Knowledge Distillation in Federated Learning
  6. Federated Learning for Generalized Optimization
  7. Federated Learning for Personalized Optimization
  8. Methodology
  9. Problem Formulation
  10. Knowledge Distillation
  11. Hierarchical Knowledge Structuring (HKS)
  12. HKS Training Framework
  13. Personalized Federated Learning with HKS
  14. Experiments and Evaluations
  15. Experimental Setup
  16. ...and 6 more sections

Figures (3)

  • Figure 1: Overview of federated learning frameworks with edge devices, highlighting the key distinguishing features of FedAvg, FedDistill, and FedCache (marked in red). FedAvg necessitates homogeneous model architectures and faces challenges such as high communication overhead and privacy concerns due to the sharing of model parameters. FedDistill relies on coarse-grained logit exchange, which is insufficient for achieving effective personalized federated learning (PFL). FedCache involves sharing client-labels, raising privacy risks, and lacks a hierarchical structure to control the granularity of knowledge exchange.
  • Figure 2: Overview of the HKS framework.
  • Figure 3: Cluster path of a target sample in the bottom-up hierarchical clustering. The top-most node represents the initial cluster containing the target sample. Each subsequent row represents a merged cluster showing member samples.