Coordination on a Budget: Federated Active Learning with Few Labels

2026-08-19

AI Summary

研究在低预算下通过联邦主动学习解决数据隐私和标签稀缺问题，提出一种新的框架利用联邦表示学习来对齐客户端数据，实现全局协调的主动选择。

Abstract

Federated Active Learning (FAL) addresses the dual challenges of data privacy and label scarcity, where the absence of a global data view introduces additional hurdles for coordinated query selection. We study cross-silo FAL in the low-budget regime, where annotation decisions are most critical. We characterize, both theoretically and empirically, a heterogeneity reversal: in low-budget settings, homogeneous (IID) data requires stronger coordination to avoid redundant queries, whereas heterogeneous data naturally promotes diversity; this trend reverses at higher budgets. Thus, in contrast to the standard federated learning (FL) narrative where heterogeneity is a primary challenge, we show that IID settings are more challenging for query selection in FAL. Motivated by these findings, we propose a new FAL framework that utilizes federated representation learning to align client data in a shared embedding space. This enables the server to perform globally coordinated active selection over optionally obfuscated client embeddings, while annotation remains local to each client. Although our framework operates in the more challenging low-budget regime, it achieves performance that surpasses existing FAL methods even when they are given substantially larger annotation budgets, demonstrating the value of centralized coordination under privacy constraints.

Problem

• Federated Active Learning
• Label Scarcity
• Data Privacy
• Query Selection
• Budget Constraints

Innovation

• Federated Active Learning
• Federated Representation Learning
• Low-budget Regime
• Data Privacy
• Global Coordination