grammar

Author: oguzhan-baser

_posts/2025-09-10-FairSynergy.md

@@ -68,15 +68,10 @@ We introduce FairSynergy, a novel framework to allocate cloud resources fairly a
 We compare our method to common baselines and standard fair allocation methods:
 
 - **Fair-Synergy (Ours)** allocates compute (and labels) to equalize next-unit accuracy gains per agent using fitted concave utilities.
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 - **Random Allocation** splits the available compute (and labels) at random among agents.
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 - **Uniform Allocation** gives every agent the same share of compute (and labels), ignoring local differences.
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 - **Classical NUM** optimizes a uniform log utility (identical response curves), so allocation follows equalized marginal gains without task-specific reweighting and agent heterogeneity.
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 - **Dominant Resource Fairness (DRF)** equalizes each agent’s dominant resource share, targeting max–min fairness across resources.
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 - **Leximin** prioritizes the worst-off first, maximizing the minimum utility, then the next, and so on. It is a stricted form of fairness compared to other methods.
 
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