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This paper considers an extended Gutoski-Watrous game in which each player's quantum strategy is represented by a local density matrix, and derives tensor-contraction expressions for the payoff functions and their gradients, thereby avoiding the explicit construction of the full joint density matrix.
SensCluster is proposed, a novel sensitivity-aware CFL framework that constructs compact client representations by selecting parameters that are most responsive to local feature distributions, and consistently outperforms state-of-the-art CFL methods across diverse feature skew scenarios.
The Unitary Strategy Matrix Exponential Algorithm (USMEA) is proposed, a geometry-aware sequential algorithm for the EEWL mixed-strategy setting, in which each player jointly learns a trainable set of local unitary actions and the associated classical mixing probabilities.
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, but its performance degrades significantly under heterogeneous data distributions. While Clustered Federated Learning (CFL) mitigates heterogeneity by grouping similar clients, most existing methods primari...
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