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The results support the conclusion: foundation-model supervision can amplify a fixed manual annotation budget into a substantially larger training set and yield a compact, deployable edge model.
It is concluded that the positions of sparse activations should be treated as sensitive transmitted data and audited carefully in the context of collaborative inference, and that sparsification reduces the leakage far less than it reduces the transmission cost.
An SVD-based LLM compression framework that guides each weight matrix's low-rank approximation with a backward-signal influence metric, which improves perplexity over SVD-LLM(W) by>18% at<=60% parameter retention, matches its quality with ~90% less calibration data, and turns parameter savings into FLOP, peak-memory, a...
FedKT-CSD (Federated Knowledge Transfer via Collaborative Synthetic Data), a framework inspired by neural image compression that closes the gap in jointly achieving low communication, robustness to heterogeneity, and rigorous privacy by leveraging publicly pretrained autoencoders as a shared latent space.
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