Recent years have witnessed an increasing use of coordinated accounts on social media, operated by misinformation campaigns to influence public opinion and manipulate social outcomes. Consequently, there is an urgent need to develop an effective methodology for coordinated group detection to combat the misinformation on social media. However, the sparsity of account activities on social media limits the performance of existing deep learning based coordination detectors as they can not exploit useful prior knowledge. Instead, the detectors incorporated with prior knowledge suffer from limited expressive power and poor performance. Therefore, in this paper we propose a coordination detection framework incorporating neural temporal point process with prior knowledge such as temporal logic or pre-defined filtering functions. Specifically, when modeling the observed data from social media with neural temporal point process, we jointly learn a Gibbs distribution of group assignment based on how consistent an assignment is to (1) the account embedding space and (2) the prior knowledge.

We present more experiments and provide all missing proofs in the appendix. Concretely, Appendix A describes the experiment setup and contains additional numerical experiments. Appendix B and C provide the detailed proofs for our unified privacy guarantee in Theorem 2 and unified utility and communication complexity analysis in Theorem 3, respectively. Appendix D provides the proof for CDP-SGD (Theorem 1). Finally, Appendix E provides the proofs for Section 5, including Lemma 1 (showing that several local gradient estimators satisfy the generic Assumption 3) and Corollaries 1-3 (instantiating Lemma 1 in the unified Theorem 3) for the proposed SoteriaFL-style algorithms.

To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient federated learning algorithms with the aid of communication compression. On the other end, privacy-preserving, especially at the client level, is another important desideratum that has not been addressed simultaneously in the presence of advanced communication compression techniques yet. In this paper, we propose a unified framework that enhances the communication efficiency of private federated learning with communication compression. Exploiting both general compression operators and local differential privacy, we first examine a simple algorithm that applies compression directly to differentially-private stochastic gradient descent, and identify its limitations. We then propose a unified framework SoteriaFL for private federated learning, which accommodates a general family of local gradient estimators including popular stochastic variance-reduced gradient methods and the state-of-the-art shifted compression scheme. We provide a comprehensive characterization of its performance trade-offs in terms of privacy, utility, and communication complexity, where SoteriaFL is shown to achieve better communication complexity without sacrificing privacy nor utility than other private federated learning algorithms without communication compression.

We study the problem of list-decodable linear regression, where an adversary can corrupt a majority of the examples. Specifically, we are given a set T of labeled examples (x,y) Rd R and a parameter 0 <α<1/2 such that an α-fraction of the points in T are i.i.d.

Figure 8: Visualizations of accuracy and bias flows for the smaller ANN trained on the synthetic dataset. Note how the most dominant accuracy flows arise from X3, which is the only bias-free feature in the dataset. In contrast, the largest bias flows arise from X1 and X2, both of which are heavily biased features. It is intuitively clear from these pictures which edges have the largest bias-to-accuracy flow ratios, and hence which edges would be the first to be pruned.