LT Codes with Feedback: Accelerate the Distributed MatrixVector Multiplication with Stragglers In this paper, we propose a coding scheme for distributed matrixvector multiplication that builds upon the Luby transform (LT) codes with feedback. The ideal soliton distribution is utilized in our LT coding scheme to encode the submatrices. Besides, the belief propagation (BP) decoding algorithm is modified to cooperate with the feedback information. Compared with other coded distributed computations with straggling servers, our approach achieves lower computation latency when the overall delay incurred by the encoding, mapping and decoding process is considered. Furthermore, we compare the storage loads of different schemes and show that the LT coding with feedback has a strong comparative advantage in these stragglertolerant computation scenarios.

HighDimensional Stochastic Gradient Quantization for CommunicationEfficient Edge Learning Edge machine learning involves the deployment of machine learning algorithms at the network edge so as to leverage massive mobile data and distributed computation resources. Many edge learning frameworks (e.g., federated learning) have been developed based on distributed gradient descent. Based on the approach, stochastic gradients are computed at edge devices and then transmitted to an edge server for aggregation for updating a global AI model. Since each gradient is typically highdimensional (with millions to billions of coefficients), communication overhead may become a bottleneck for edge learning. In this work, we propose a novel gradient compression scheme to reduce the aforementioned overhead. Specifically, in the proposed scheme, the norm of the stochastic gradient is quantized using a uniform quantizer while the normalized stochastic gradient is decomposed into block gradients. A Grassmannian codebook is applied to quantizing each normalized block gradients. Their quantized versions are assembled using a socalled hinge vector, which is quantized using another Grassmannian codebook. Furthermore, a practical bitallocation strategy is developed. By simulations, we show that similar learning performance can be achieved with substantially lower communication overhead as compared to the onebit scalar quantization schemes used in the stateoftheart design, namely signed SGD.