Genko 原稿Back to edition

Monday, 2 Nov 2026 · cs.CR · cs.DC · cs.LG · crossfield

DICE: Decentralized Incentive-driven Federated Learning for Efficient & Scalable Customization of Pretrained Models in Web3

Customizing Pretrained Models (PMs) for downstream tasks demands task-specific data, which burgeoning Web3 applications provide abundantly.

By Hong Lin, Zhongle Xie, Lidan Shou et al · 1-minute read

Explainer · on the wire

The explainer for this story is still on the wire.

The claim, the mechanism, and the figures land here once generated.

Source · arXiv

arxiv.org/abs/2610.39132cs.CR · cs.DC · cs.LG

More from this edition

Surprise Alone Learns Lies: Evidence-Gated Continual Learning for LLMs from Streaming ClaimsHow Specific Do I Need to Be? Effects of User Query Underspecification on Deep Research AgentsYoutu-Parsing-Omni: One Encoder, One Schema, All ModalitiesIncentivizing Self-Learning in Large Language Models via Reinforcement Learning