Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables multiple clients to collaboratively train statistical models without disclosing raw training data. However, the inaccessible local training data and uninspectable local training process make FL susceptible to various Byzantine attacks (e.g., data poisoning and model poisoning attacks), aiming to manipulate the FL model training process and degrade the model performance. Most of the existing Byzantine-robust FL schemes cannot effectively defend against stealthy poisoning attacks that craft poisoned models statistically similar to benign models. Things worsen when many clients are compromised or data among clients are highly non-independent and identically distributed (non-IID). In this work, to address these issues, we propose FedInv, a novel Byzantine-robust FL framework by inversing local model updates. Specifically, in each round of local model aggregation in FedInv, the parameter server first inverses the local model updates submitted by each client to generate a corresponding dummy dataset. Then, the server identifies those dummy datasets with exceptional Wasserstein distances from others and excludes the related local model updates from model aggregation. We conduct an exhaustive experimental evaluation of FedInv. The results demonstrate that FedInv significantly outperforms the existing robust FL schemes in defending against stealthy poisoning attacks under highly non-IID data partitions.

With the development and increasing popularity of information technology, how to effectively receive and manage the information from the internet becomes a more pronounced problem. According to certain standards, users can take some methods of information filtering to block the information they do not want and retrieve the information they want. In this way, people can effectively avoid false information; spam; commercial advertising; insulting, internet attacks; pornography; and other internet problems. This paper focuses on information filtering systems against information pollution and crime, describes the characteristics and methods of information filtering, and introduces some information filtering software.

Supernumerary cusp on the bucca of left maxillary second molar is a rare phenomenon, which is difficult to be differentiated from other tooth deformities. CBCT can improve accuracy of diagnosis.

Nelson, Phillips, Enfinger, and Steuart's book is about the science of computer forensics and its implications in crime investigations. This book is not intended to provide comprehensive training in computer forensics, but introduce the science the science of computer forensics and its implications in crime investigations. It focused on establishing a solid foundation for those who are new to this field. Nelson, Philips, Enfiger, and Steuart are experienced experts in different areas of computer forensics. Different expertise makes this book could benefit many groups of people at different educational level and industrial background.

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