Deeper guide: FL algorithms, FL setups, and worked sketches beyond MNIST
维护者通常 1 天内回复
还没有人认领这个 Issue。
评估
- 难度
- 5/5
- 预计耗时
- 一周以上
- 新手友好度
- 48/100
- Issue 类型
- 文档
- 描述清晰度
- 描述清楚
- 活跃度
- 活跃
- 技术栈
- python
调研方向
Start with Documentation/public/what-can-be-trained.md and services.md, then inspect client.py and aggregator.py to map the listed algorithms and FL setups to the train → audit → aggregate loop. Keep tested behavior distinct from untested sketches, and cover evaluation beyond MNIST. Done when the companion page is linked from both existing pages and gives model owners the requested service map and two non-MNIST sketches.
由索引模型根据 Issue 内容生成。
描述
Related: issue No. 157, PR No. 189, P3-DOC1, P3-DOC4
Why this would help
Issue No. 157 produced Documentation/public/what-can-be-trained.md, merged in PR No. 189. That page deliberately stays short and separates what has been tested from what is only possible. The proposed outline on issue No. 157 also had deeper sections that weren't shipped. A model owner who already knows FL can't yet see how a particular algorithm or FL setup maps onto DIN's train → audit → aggregate loop, or what a non-MNIST task would look like in practice.
Suggested next step
Write a companion page for readers who want more detail, linked from what-can-be-trained.md and services.md. Keep the tested-vs-possible labeling the current page uses, and cover:
- FL algorithms: how FedAvg, FedProx, FedOpt (FedAdam/FedYogi/FedAdagrad), SCAFFOLD, FedNova and q-FFL fit the GI loop, and which service file each one touches (
client.py,aggregator.py). Also how Byzantine-robust aggregators (Krum, trimmed mean, coordinate-wise median) sit alongside auditor scoring. - FL setups: horizontal (the default), vertical (entity alignment, split-model services), heterogeneity-aware (HeteroFL/FjORD-style sub-models), personalized, and federated transfer learning. For each, what changes in the services.
- Worked sketches, each clearly labeled as untested:
- a FedLoRA/FedQLoRA LLM fine-tune where only adapter weights move through IPFS
- a tabular regression model with its own scoring function
- How results are evaluated beyond MNIST: choosing a held-out test set and a scoring function that fits the task, and how that relates to auditor/BlockFLow scoring.
Expected outcome
A model owner can see which service changes their algorithm or FL setup needs, and has at least two non-MNIST sketches to start from. Nothing untested is presented as supported.
Turning any of these sketches into a tested reference implementation is a separate task.
- 主要语言
- Python
- 星标
- 4
- 派生
- 7
- 平均合并
- 3 天 4 小时
- 30 天内合并 PR
- 34
环境准备
这个项目没有提供开发容器、Dockerfile 或贡献指南，环境需要你自己搭建：先看它的 README，通用步骤见我们的新手贡献指南。
从这里开始
- 先读完整个 Issue，再读项目的贡献指南。
- 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
- Fork 仓库，在一个分支上完成修改。
- 提交 Pull Request，并在描述里引用这个 Issue 编号。
InfiniteZeroFoundation/DevNet 的其他 Issue
-
ROADMAP.md: refresh "What is still open for P3" (several items were fixed in October)可能已有人在做 @umermjd11 于 2 天前认领。 未关闭documentation good first issue
难度 2/5 1-3 小时 新手友好度 74/100
InfiniteZeroFoundation/DevNet#285 · 1 条评论 · 已指派 1 人 ·
维护者通常 1 天内回复
-
Update the 25 translated organisation READMEs to match the English version可能已有人在做 @mrCabez 今天认领。 未关闭good first issue help wanted
难度 2/5 1-3 小时 新手友好度 76/100
InfiniteZeroFoundation/DevNet#277 · 2 条评论 · 已指派 1 人 ·
维护者通常 1 天内回复
-
难度 2/5 1-3 小时 新手友好度 76/100
InfiniteZeroFoundation/DevNet#266 ·
维护者通常 1 天内回复
-
难度 5/5 一周以上 新手友好度 25/100
InfiniteZeroFoundation/DevNet#299 · 1 条评论 ·
维护者通常 1 天内回复
-
Long run
难度 5/5 一周以上 新手友好度 25/100
InfiniteZeroFoundation/DevNet#298 ·
维护者通常 1 天内回复
查看 InfiniteZeroFoundation/DevNet 的全部 Issue
相似的 Issue
-
changelog investigate
难度 2/5 1-3 小时 新手友好度 67/100
ramnes/notion-sdk-py#433 ·
-
docs pkg:core
难度 1/5 1 小时以内 新手友好度 84/100
pydantic/pydantic-ai#11929 ·
维护者通常 1 天内回复
-
难度 2/5 1-3 小时 新手友好度 78/100
intake/intake-esm#789 ·
-
难度 2/5 1-3 小时 新手友好度 82/100
MemMachine/MemMachine#1877 ·
维护者通常 3 天内回复
-
难度 2/5 1-3 小时 新手友好度 68/100
openedx/openedx-authz#579 ·
维护者通常 1 天内回复