We then propose an algorithmic template for tackling the shifted objective, which can exploit such a condition.

However, most systems of practical interest are non-linear, requiring more complex models. Many approximate inference methods have been developed for non-linear dynamical systems: Deterministic methods approximate the filtering and smoothing distributions e.g. by using a Taylor series

Proof A.1 W e firstly give Lemma 1: Lemma 1 The operation weights are caculated by a softmax function. The number of target node's intra-group neighbors is "S" indicates the setting of searching phase. "E" indicates the setting of evaluation phase. The hyper-parameter λ which controls the hidden feature smoothness is set to be 0 .125 . We show the variance of synthetic graph experiment in Table 1 to endorse our analysis in Section 3. The table shows that the variance of accuracy is relatively big in the experiment setting. However, all the results are average of 100 runs.

We propose SemiFL to address the problem of combining communication-efficient FL such as FedAvg with Semi-Supervised Learning (SSL).

We consider settings in which the data of interest correspond to pairs of ordered times, e.g, the birth times of the first and second child, the times at which a new user creates an account and makes the first purchase on a website, and the entry and survival times of patients in a clinical trial.

Additional experimental results verify these theoretical findings.