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T‖𝝃t−𝝃t(0)‖2,\boldsymbol{\xi}^{*}_{1:T}=\arg\min_{\boldsymbol{\xi}_{1:T}}\;\sum_{c\in\mathcal{C}}\sum_{t=1}^{T}\bigl[\max\!\bigl(0,\,c(\mathbf{k}_{t})\bigr)\bigr]^{2}+\lambda\sum_{t=1}^{T}\bigl\|\boldsymbol{\xi}_{t}-\boldsymbol{\xi}^{(0)}_{t}\bigr\|^{2}, (5) where 𝐤t\mathbf{k}_{t} denotes the
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Personalized federated learning gets a boost from Fed-GAME, which uses a graph attention mixture-of-experts to dynamically aggregate client updates, outperforming SOTA methods on time-series forecasting tasks.
