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Emulating Heterogeneous Client Execution in Federated Learning
FL systems are inherently subject to client heterogeneity arising from differences in hardware capabilities. We propose a realistic evaluation framework for hardware-aware federated learning methods based on lightweight emulation of client hardware. Existing evaluation approaches address this either
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