Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation

As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the lack of data auditing for untrusted clients, FL is vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned data for local training or directly changing the model parameters, attackers can easily inject backdoors into the model, which can trigger the model to make misclassification of targeted patterns in images. To address these issues, we propose a novel data-free trigger-generation-based defense approach based on the two characteristics of backdoor attacks: i) triggers are learned faster than normal knowledge, and ii) trigger patterns have a greater effect on image classification than normal class patterns. Our approach generates the images with newly learned knowledge by identifying the differences between the old and new global models, and filters trigger images by evaluating the effect of these generated images. By using these trigger images, our approach eliminates poisoned models to ensure the updated global model is benign. Comprehensive experiments demonstrate that our approach can defend against almost all the existing types of backdoor attacks and outperform all the seven state-of-the-art defense methods with both IID and non-IID scenarios. Especially, our approach can successfully defend against the backdoor attack even when 80\% of the clients are malicious

GitFL: Adaptive Asynchronous Federated Learning using Version Control

As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in AIoT (Artificial Intelligence of Things) design. However, due to the lack of efficient management of straggling devices, existing FL methods greatly suffer from the problems of low inference accuracy and long training time. Things become even worse when taking various uncertain factors (e.g., network delays, performance variances caused by process variation) existing in AIoT scenarios into account. To address this issue, this paper proposes a novel asynchronous FL framework named GitFL, whose implementation is inspired by the famous version control system Git. Unlike traditional FL, the cloud server of GitFL maintains a master model (i.e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices. By using our proposed Reinforcement Learning (RL)-based device selection mechanism, a pulled branch model with an older version will be more likely to be dispatched to a faster and less frequently selected device for the next round of local training. In this way, GitFL enables both effective control of model staleness and adaptive load balance of versioned models among straggling devices, thus avoiding the performance deterioration. Comprehensive experimental results on well-known models and datasets show that, compared with state-of-the-art asynchronous FL methods, GitFL can achieve up to 2.64X training acceleration and 7.88% inference accuracy improvements in various uncertain scenarios

Building a digital twin of EDFA: a grey-box modeling approach

To enable intelligent and self-driving optical networks, high-accuracy physical layer models are required. The dynamic wavelength-dependent gain effects of non-constant-pump erbium-doped fiber amplifiers (EDFAs) remain a crucial problem in terms of modeling, as it determines optical-to-signal noise ratio as well as the magnitude of fiber nonlinearities. Black-box data-driven models have been widely studied, but it requires a large size of data for training and suffers from poor generalizability. In this paper, we derive the gain spectra of EDFAs as a simple univariable linear function, and then based on it we propose a grey-box EDFA gain modeling scheme. Experimental results show that for both automatic gain control (AGC) and automatic power control (APC) EDFAs, our model built with 8 data samples can achieve better performance than the neural network (NN) based model built with 900 data samples, which means the required data size for modeling can be reduced by at least two orders of magnitude. Moreover, in the experiment the proposed model demonstrates superior generalizability to unseen scenarios since it is based on the underlying physics of EDFAs. The results indicate that building a customized digital twin of each EDFA in optical networks become feasible, which is essential especially for next generation multi-band network operations

Moderating effect of classroom sociable norm on the relations between unsociability and internalizing problems in Chinese adolescents

ObjectivesThe goal of the present study was to examine the moderating effect of classroom sociable norm on the relations between unsociability and internalizing problems (the indicators included depression, loneliness and self-esteem) in Chinese adolescents.MethodsParticipants were N = 1,160 adolescents in Grade 4–8 from Shanghai, People’s Republic of China. They completed questionnaires about unsociability, sociability, and social preference via peer nominations, while depression, loneliness, and self-esteem were collected via self-report.ResultsIt was found that unsociability was positively associated with depression and loneliness, and negatively associated with self-esteem. Moreover, the relations between unsociability and indicators of internalizing problems were moderated by classroom sociable norm. More specifically, the significant positive associations between unsociability and depression and loneliness were stronger in classrooms with high sociable norm, and the negative association between unsociability and self-esteem was only significant in such classrooms.ConclusionThe findings suggest that classroom sociable norm plays an important role in unsociable adolescents’ psychological adjustment in China. Researchers should focus more on the influence of classroom environment on adolescents’ development in future

Observation of photonic antichiral edge states

Chiral edge states are a hallmark feature of two-dimensional topological materials. Such states must propagate along the edges of the bulk either clockwise or counterclockwise, and thus produce oppositely propagating edge states along the two parallel edges of a strip sample. However, recent theories have predicted a counterintuitive picture, where the two edge states at the two parallel strip edges can propagate in the same direction; these anomalous topological edge states are named as antichiral edge states. Here we report the experimental observation of antichiral edge states in a gyromagnetic photonic crystal. The crystal consists of gyromagnetic cylinders in a honeycomb lattice, with the two triangular sublattices magnetically biased in opposite directions. With microwave measurement, unique properties of antichiral edge states have been observed directly, which include the titled dispersion, the chiral-like robust propagation in samples with certain shapes, and the scattering into backward bulk states at certain terminations. These results extend and supplement the current understanding of chiral edge states

Exploring Blockchain Technology through a Modular Lens: A Survey

Blockchain has attracted significant attention in recent years due to its potential to revolutionize various industries by providing trustlessness. To comprehensively examine blockchain systems, this article presents both a macro-level overview on the most popular blockchain systems, and a micro-level analysis on a general blockchain framework and its crucial components. The macro-level exploration provides a big picture on the endeavors made by blockchain professionals over the years to enhance the blockchain performance while the micro-level investigation details the blockchain building blocks for deep technology comprehension. More specifically, this article introduces a general modular blockchain analytic framework that decomposes a blockchain system into interacting modules and then examines the major modules to cover the essential blockchain components of network, consensus, and distributed ledger at the micro-level. The framework as well as the modular analysis jointly build a foundation for designing scalable, flexible, and application-adaptive blockchains that can meet diverse requirements. Additionally, this article explores popular technologies that can be integrated with blockchain to expand functionality and highlights major challenges. Such a study provides critical insights to overcome the obstacles in designing novel blockchain systems and facilitates the further development of blockchain as a digital infrastructure to service new applications

The moderating effects of parental psychological control on the relationship between unsociability and socio-emotional functioning among Chinese children

IntroductionThere have been studies indicating that children’s unsociability was associated with poorer socio-emotional functioning in China. Although some researchers have found that parenting behavior would influence the relationship between children’s unsociability and adjustment, the role of parental psychological control has not been explored. This study aimed to investigate the moderating effect of parental psychological control on the relationship between unsociability and socio-emotional functioning in Chinese children.MethodsA total of 1,275 students from Grades 3 to 7 (637 boys, Mage = 10.78 years, SD = 1.55 years) were selected from four public schools in Shanghai to participate in this study. Data of unsociability, peer victimization and social preference were collected from peer-nominations, and data of parental psychological control, depressive symptoms and social anxiety were collected from self-reports.ResultsThere were positive associations between unsociability and peer victimization, depressive symptoms, and social anxiety, as well as a negative association between unsociability and social preference. Parental psychological control moderated these associations, specifically, the associations between unsociability and peer victimization, social preference, and depressive symptoms were stronger, and the association between unsociability and social anxiety was only significant among children with higher level of parental psychological control.DiscussionThe findings in the current study highlight the importance of parental psychological control in the socio-emotional functioning of unsociable children in the Chinese context, enlightening educators that improving parenting behavior is essential for children’s development

Roadmap on chalcogenide photonics

Alloys of sulfur, selenium and tellurium, often referred to as chalcogenide semiconductors, offer a highly versatile, compositionally-controllable material platform for a variety of passive and active photonic applications. They are optically nonlinear, photoconductive materials with wide transmission windows that present various high- and low-index dielectric, low-epsilon and plasmonic properties across ultra-violet, visible and infrared frequencies, in addition to an, non-volatile, electrically/optically induced switching capability between phase states with markedly different electromagnetic properties. This roadmap collection presents an in-depth account of the critical role that chalcogenide semiconductors play within various traditional and emerging photonic technology platforms. The potential of this field going forward is demonstrated by presenting context and outlook on selected socio-economically important research streams utilizing chalcogenide semiconductors. To this end, this roadmap encompasses selected topics that range from systematic design of material properties and switching kinetics to device-level nanostructuring and integration within various photonic system architectures