First, wepresent a unified framework that encompasses previous methods.

Recently,therehasbeenanincreasing interestindevelopment of Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI.

The first two permit using subgradients andstochastic gradients, andareshowntoensureaO(1/ t) convergencerate.

The first two permit using subgradients andstochastic gradients, andareshowntoensureaO(1/ t) convergencerate.

The out-side lab doesn't7 have access to in-hospital's private labels, but it could still initiate and provide assistance to the in-hospital lab by8 fitting the received residuals instead of public labels. An example case is where each participant holds a disjoint subset of the features and uses linear regression. Wealso did extensiveexperimentsin the last few days to provide empirical evidence (summarized in18 Table1). Each participant needs to hold and distribute identifiers for data23 items, so that the data from different participants can be conceptually combined. Theprobabilityofchoosing34 the optimum could be theoretically derived from large-deviation bounds (under certain assumptions).