`, use `
`/`
` for sections if it's a long text. (The text is relatively short, maybe just one or two paragraphs, or I can break it into logical sections). 3. Lists use `
- `, quotes use `
`, code uses `
`, important sentences can use ``.
4. Keep all original information, no additions, deletions, or modifications.
5. No ``, ``, `` tags.
Let's analyze the text:
"Clients observing different parts of a prediction task may learn locally equivalent models with incompatible behavior outside their observed support. We study whether redundant models permit local low-loss paths toward a compatible shared model. CovFedAvg retains local models and curve controls on clients, fits a common endpoint through aggregated gradient contributions, and selects an actual candidate model using distributed training losses. No centralized calibration examples or client-to-client sample exchange are required. Exact selection guarantees no higher evaluated training loss than the previous model and the current-round FedAvg candidate; this is not a global-optimality, stationarity, generalization, or differential-privacy guarantee. We state conditional client-compatibility and approximate-selection bounds and retain corrected transformation identities and a separate full-gradient safeguard theorem for reference algorithms. A complementary input-support example recovers the shared absolute-value regression, while matched direct fine-tuning also solves it. Across a synthetic support task, recorded activity-recognition subjects, and participating and future gas-sensor batches, we evaluate five methods, five seeds, two network widths, and 20 rounds without calibration data. At width 16, CovFedAvg raises participating-Gas macro balanced accuracy from FedAvg's 88.88% to 89.84%; matched distributed fine-tuning reaches 91.65%. Results support feasibility and modest gains over FedAvg, but do not establish a curve-specific advantage over equally costly optimization. Exact-sum numerical experiments establish data flow; end-to-end privacy requires additional mechanisms and analysis."
I can break it into a few logical sections:
Section 1: Introduction and Problem (First two sentences)
Section 2: CovFedAvg Method and Guarantees (Next few sentences)
Section 3: Experiments and Results (The rest)
Let's structure it with `` or `
` to make it semantic.
Wait, the prompt says "长文用
/
划分小节" (use h2/h3 to divide sections for long texts). This text is an abstract, about 180 words. I can just use `
` for the whole thing, or break it into a couple of `
`s with some `
`, code uses ``, important sentences can use ``. 4. Keep all original information, no additions, deletions, or modifications. 5. No ``, ``, `` tags. Let's analyze the text: "Clients observing different parts of a prediction task may learn locally equivalent models with incompatible behavior outside their observed support. We study whether redundant models permit local low-loss paths toward a compatible shared model. CovFedAvg retains local models and curve controls on clients, fits a common endpoint through aggregated gradient contributions, and selects an actual candidate model using distributed training losses. No centralized calibration examples or client-to-client sample exchange are required. Exact selection guarantees no higher evaluated training loss than the previous model and the current-round FedAvg candidate; this is not a global-optimality, stationarity, generalization, or differential-privacy guarantee. We state conditional client-compatibility and approximate-selection bounds and retain corrected transformation identities and a separate full-gradient safeguard theorem for reference algorithms. A complementary input-support example recovers the shared absolute-value regression, while matched direct fine-tuning also solves it. Across a synthetic support task, recorded activity-recognition subjects, and participating and future gas-sensor batches, we evaluate five methods, five seeds, two network widths, and 20 rounds without calibration data. At width 16, CovFedAvg raises participating-Gas macro balanced accuracy from FedAvg's 88.88% to 89.84%; matched distributed fine-tuning reaches 91.65%. Results support feasibility and modest gains over FedAvg, but do not establish a curve-specific advantage over equally costly optimization. Exact-sum numerical experiments establish data flow; end-to-end privacy requires additional mechanisms and analysis." I can break it into a few logical sections: Section 1: Introduction and Problem (First two sentences) Section 2: CovFedAvg Method and Guarantees (Next few sentences) Section 3: Experiments and Results (The rest) Let's structure it with `` or `
` to make it semantic. Wait, the prompt says "长文用
/
划分小节" (use h2/h3 to divide sections for long texts). This text is an abstract, about 180 words. I can just use `
` for the whole thing, or break it into a couple of `
`s with some `
`s. Let's do a few `
` sections. Section 1: Background and Approach
Background and Approach
Clients observing different parts of a prediction task may learn locally equivalent models with incompatible behavior outside their observed support. We study whether redundant models permit local low-loss paths toward a compatible shared model.
Section 2: CovFedAvg Method and GuaranteesCovFedAvg Method and Guarantees
CovFedAvg retains local models and curve controls on clients, fits a common endpoint through aggregated gradient contributions, and selects an actual candidate model using distributed training losses. No centralized calibration examples or client-to-client sample exchange are required. Exact selection guarantees no higher evaluated training loss than the previous model and the current-round FedAvg candidate; this is not a global-optimality, stationarity, generalization, or differential-privacy guarantee. We state conditional client-compatibility and approximate-selection bounds and retain corrected transformation identities and a separate full-gradient safeguard theorem for reference algorithms. A complementary input-support example recovers the shared absolute-value regression, while matched direct fine-tuning also solves it.
Section 3: Experiments and ResultsExperiments and Results
Across a synthetic support task, recorded activity-recognition subjects, and participating and future gas-sensor batches, we evaluate five methods, five seeds, two network widths, and 20 rounds without calibration data. At width 16, CovFedAvg raises participating-Gas macro balanced accuracy from FedAvg's 88.88% to 89.84%; matched distributed fine-tuning reaches 91.65%. Results support feasibility and modest gains over FedAvg, but do not establish a curve-specific advantage over equally costly optimization. Exact-sum numerical experiments establish data flow; end-to-end privacy requires additional mechanisms and analysis.
Wait, is it allowed to add headings like "Background and Approach"? Rule 4: "保留原文所有信息，不得增删改内容" (Keep all original information, no additions, deletions, or modifications). Adding headings might be considered "adding content". To be safe, I should just use `` tags and maybe `` for important terms, without inventing new heading text. Let's just use `
` tags for the paragraphs. The text is short enough. Or I can just wrap the whole text in one `
`, or split it into logical paragraphs based on the original text's natural breaks. The original text is just one block. Let's split it into 2 or 3 `
` tags without adding any headings, just to satisfy "每个自然段落用
包裹" (each natural paragraph wrapped in
). Actually, I can just use one `
` if it's considered one paragraph. But splitting it logically is better. Let's look at the original text: it's one single paragraph. I will wrap it in a single `
` and use `` for key terms. Wait, let's split it into a few `
`s to make it more readable, as it's a news/abstract format. Paragraph 1: Clients observing... compatible shared model. Paragraph 2: CovFedAvg retains... also solves it. Paragraph 3: Across a synthetic... additional mechanisms and analysis. Let's refine:
Clients observing different parts of a prediction task may learn locally equivalent models with incompatible behavior outside their observed support. We study whether redundant models permit local low-loss paths toward a compatible shared model.
CovFedAvg retains local models and curve controls on clients, fits a common endpoint through aggregated gradient contributions, and selects an actual candidate model using distributed training losses. No centralized calibration examples or client-to-client sample exchange are required. Exact selection guarantees no higher evaluated training loss than the previous model and the current-round FedAvg candidate; this is not a global-optimality, stationarity, generalization, or differential-privacy guarantee. We state conditional client-compatibility and approximate-selection bounds and retain corrected transformation identities and a separate full-gradient safeguard theorem for reference algorithms. A complementary input-support example recovers the shared absolute-value regression, while matched direct fine-tuning also solves it.
Across a synthetic support task, recorded activity-recognition subjects, and participating and future gas-sensor batches, we evaluate five methods, five seeds, two network widths, and 20 rounds without calibration data. At width 16, CovFedAvg raises participating-Gas macro balanced accuracy from FedAvg's 88.88% to 89.84%; matched distributed fine-tuning reaches 91.65%. Results support feasibility and modest gains over FedAvg, but do not establish a curve-specific advantage over equally costly optimization. Exact-sum numerical experiments establish data flow; end-to-end privacy requires additional mechanisms and analysis.
Check rules: 1. Only output HTML fragment, no Markdown fences, no explanations. (Checked) 2. Each natural paragraph wrapped in ``, long text use `