Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning

2024-03-05 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision, Natural Language Processing · Depth: Expert, extended

Summary

FedTSP is a novel Federated Prototype Learning (FedPL) method designed to address data and model heterogeneity in Federated Learning (FL) by incorporating textual semantics. Existing FedPL approaches often prioritize inter-class prototype discrimination, inadvertently disrupting crucial semantic relationships. FedTSP overcomes this by leveraging a Large Language Model (LLM) to generate fine-grained textual descriptions for each class, which a Pre-trained Language Model (PLM) then processes on the server to create semantically rich textual prototypes. To bridge the modality gap between these textual prototypes and client-side image models, FedTSP introduces trainable prompts that adapt the prototypes to specific client tasks. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet demonstrate that FedTSP significantly outperforms state-of-the-art methods in heterogeneous FL (HtFL), General FL (GFL), and Personalized FL (PFL) settings, achieving up to 4.20% higher accuracy and accelerating convergence.

Key takeaway

For research scientists developing federated learning solutions, FedTSP offers a robust approach to improve model performance and convergence speed, especially in highly heterogeneous environments. You should consider integrating LLM-generated textual semantics and trainable prompts into your prototype-based FL frameworks to enhance inter-class semantic preservation and bridge modality gaps, leading to more accurate and generalizable models across diverse client data and model architectures.

Key insights

Textual semantics from LLMs and PLMs can significantly enhance prototype quality and model generalization in heterogeneous federated learning.

Principles

• Preserve inter-class semantic relationships for better model generalization.
• External semantic knowledge can mitigate data heterogeneity issues.
• Trainable prompts bridge modality gaps between text and image features.

Method

FedTSP uses an LLM for fine-grained class descriptions, a PLM to create textual prototypes, and trainable prompts to align these with client image models, employing contrastive loss for feature alignment during local training.

In practice

• Use LLMs to generate rich class descriptions for prototypes.
• Employ trainable prompts to adapt text-based prototypes to visual tasks.
• Apply contrastive loss for robust feature alignment in heterogeneous FL.

Topics

• Federated Prototype Learning
• Textual Semantics
• Large Language Models
• Pre-trained Language Models
• Data Heterogeneity

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.