Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many devices to collaboratively train a global model without sharing their private data. To enhance the training efficiency of FL, various algorithms have been proposed, ranging from first-order second-order methods. However, these cannot be applied in scenarios where gradient information is not available, e....

We study a class of cooperative multi-agent optimization problems, where each agent is associated with local action vector and cost, the goal to cooperatively find joint profile that minimizes average costs. consider setting gradient information not readily available, agents only observe their costs incurred by actions as feedback determine new actions. propose zeroth-order scheme provide expli...

A topological index is a function having set of graphs as its domain and real numbers range. Here we concentrated on indices involving the number vertices, edges maximum minimum vertex degree. The aim this paper to compute lower upper bounds second Zagreb index, third Hyper Harmonic Redefined first First reformulated Forgotten square F-index, Sum-connectivity Randic Reciprocal Gourava Sombar Ni...

Due to complexity and delay constraints a usually significant amount of residual redundancy remains in the source samples after source coding. This residual redundancy can be exploited by iterative source-channel decoding for error concealment and quality improvements. One key design issue in joint source-channel (de-)coding is the index assignment. Besides conventional index assignments optimi...