At least five people have been killed in Israeli attacks on several towns in southern Lebanon, the country's Health Ministry has said, amid a fragile ceasefire between Israel and Hezbollah. "An Israeli enemy drone strike on the town of Ainata killed one person and wounded another," the ministry said. An "Israeli strike on the town of Bint Jbeil killed three people," while a third "on Beit Lif killed one person", it added. There was no immediate comment from the Israeli military on the attacks. Israel's army escalated its attacks on Lebanon in late September after more than 11 months of cross-border exchanges of fire with the Lebanese armed group Hezbollah, which began firing rockets towards Israel after the Palestinian group Hamas's attack on southern Israel on October 7, 2023.

Distributed approaches have many computational benefits, but they are vulnerable to attacks from a subset of devices transmitting incorrect information. This paper investigates Byzantine-resilient algorithms in a decentralized setting, where devices communicate directly with one another. We investigate the notion of breakdown point, and show an upper bound on the number of adversaries that decentralized algorithms can tolerate. We introduce $\mathrm{CG}^+$, an algorithm at the intersection of $\mathrm{ClippedGossip}$ and $\mathrm{NNA}$, two popular approaches for robust decentralized learning. $\mathrm{CG}^+$ meets our upper bound, and thus obtains optimal robustness guarantees, whereas neither of the existing two does. We provide experimental evidence for this gap by presenting an attack tailored to sparse graphs which breaks $\mathrm{NNA}$ but against which $\mathrm{CG}^+$ is robust.

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been proposed to enhance the robustness of D-SGD to such machines, previous works either resort to strong assumptions (trusted server, homogeneous data, specific noise model) or impose a gradient computational cost that is several orders of magnitude higher than that of D-SGD. We present MoNNA, a new algorithm that (a) is provably robust under standard assumptions and (b) has a gradient computation overhead that is linear in the fraction of faulty machines, which is conjectured to be tight. Essentially, MoNNA uses Polyak's momentum of local gradients for local updates and nearest-neighbor averaging (NNA) for global mixing, respectively. While MoNNA is rather simple to implement, its analysis has been more challenging and relies on two key elements that may be of independent interest. Specifically, we introduce the mixing criterion of $(\alpha, \lambda)$-reduction to analyze the non-linear mixing of non-faulty machines, and present a way to control the tension between the momentum and the model drifts. We validate our theory by experiments on image classification and make our code available at https://github.com/LPD-EPFL/robust-collaborative-learning.

Whether stochastic or parametric, the Pareto/NBD model can only be utilized for an in-sample prediction rather than an out-of-sample prediction. This research thus provides a neural network based extension of the Pareto/NBD model to estimate the out-of-sample parameters, which overrides the estimation burden and the application dilemma of the Pareto/NBD approach. The empirical results indicate that the Pareto/NBD model and neural network algorithms have similar predictability for identifying inactive customers. Even with a strong trend fitting on the customer count of each repeat purchase point, the Pareto/NBD model underestimates repeat purchases at both the individual and aggregate levels. Nonetheless, when embedding the likelihood function of the Pareto/NBD model into the loss function, the proposed parameter estimation method shows extraordinary predictability on repeat purchases at these two levels. Furthermore, the proposed neural network based method is highly efficient and resource-friendly and can be deployed in cloud computing to handle with big data analysis.

SUNNYVALE, Calif., May 11, 2018 –Socionext Inc., a leading provider of SoC-based solutions, has developed a new Neural Network Accelerator (NNA) engine, optimized for AI processing on edge computing devices. The compact, low power engine has been designed specifically for deep learning inference processing. When implemented, it can achieve 100x performance boost compared with conventional processors for computer vision processing such as image recognition. Socionext will start delivering the Software Development Kit for the FPGA implementation of the NNA in the third quarter of 2018. The company is also planning to develop its SoC products with the NNA.