S2-WEF detects dynamic free-riders in federated learning by simulating attack WEF patterns from prior global models, combining them with mutual deviation scores, and using two-dimensional clustering without proxy data or pre-training.
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Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.
HT-SHiLo decoding algorithm improves SNR and doubles imaging depth in OS-SIM for thick tissues and live cells.
citing papers explorer
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Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns
S2-WEF detects dynamic free-riders in federated learning by simulating attack WEF patterns from prior global models, combining them with mutual deviation scores, and using two-dimensional clustering without proxy data or pre-training.
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On What We Can Learn from Low-Resolution Data
Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.
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High SNR 3D Imaging from Millimeter-scale Thick Tissues to Cellular Dynamics via Structured Illumination Microscopy
HT-SHiLo decoding algorithm improves SNR and doubles imaging depth in OS-SIM for thick tissues and live cells.