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d953030747
* feat: add v1/v2 versioning and language selector for mdbook - Copy current content to v1/ directory (1st Edition) - Create v2/ directory with new TOC structure (2nd Edition) and placeholder chapters - Add version selector (V1/V2) and language toggle (EN/ZH) in top-right nav bar - Add build scripts: build_mdbook_v1.sh, build_mdbook_v2.sh - Update assemble_docs_publish_tree.py to support v1/v2 deployment layout - Fix mdbook preprocessor to use 'sections' key (v0.4.43 compatibility) - Update .gitignore for new build artifact directories - Deployment layout: / = v2 EN, /cn/ = v2 ZH, /v1/ = v1 EN, /v1/cn/ = v1 ZH Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * build: update CI to build and verify all four books (v1/v2 x EN/ZH) - Clarify step names: "Build v2 (EN + ZH)" and "Build v1 (EN + ZH)" - Add verification step to check all four index.html outputs exist - Deploy workflow assembles: / = v2 EN, /cn/ = v2 ZH, /v1/ = v1 EN, /v1/cn/ = v1 ZH Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: gracefully skip missing TOC entries instead of crashing resolve_toc_target() now returns None for missing files instead of raising FileNotFoundError. This fixes v1 EN build where chapter index files reference TOC entry names that don't match actual filenames. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
720 B
720 B
小结
在这一章，我们简单介绍了联邦学习的背景、系统架构、联邦平均算法、隐私加密算法以及实际部署时的挑战。联邦学习是一个新起步的人工智能算法，可以在“数据保护”与“数据孤岛”这两大约束条件下，建立有效的机器学习模型。此外，由于联邦学习场景的特殊性（端侧数据不上传、安全隐私要求高和数据非独立同分布等特点），使得系统和算法的开发难度更高：如何平衡计算和通讯的开销，如何保证模型不会泄露隐私，算法如何在非独立同分布场景下收敛。这些难点都需要开发人员对实际的联邦学习场景有更深刻的认识。