Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddings
Federated learning (FL) performance is highly sensitive to heterogeneity across clients, yet practitioners lack reliable methods to anticipate how a federation will behave before training. We propose readiness indices, derived from Task2Vec embeddings, that quantifies the alignment of a federation prior to training and correlates with its eventual performance. Our approach computes unsupervised metrics -- such as cohesion, dispersion, and density -- directly from client embeddings. We evaluate t
Record details
Published: 12 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026
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ethics.ai (12 April 2026), “Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddings,” evidence record 5977, https://ethics.ai/record/5977 (originally published by arXiv).
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