Information reliability in complex multitask networks

The emergence of distributed and complex networks has altered the field of information and data processing in the past few years. In distributed networks, the connected neighboring nodes can cooperate and share information with each other in order to solve particular tasks. However, in many applications the agents might be reluctant to share their true data with all their neighbors due to privacy and security constraints. In this paper, we study the performance of multitask distributed networks where sharing genuine information is subject to a cost. We formulate an information credibility model which results in the probability of sharing genuine information at each time instant according to the cost. Each agent then shares its true information with only a subset of its neighbors while sending fabricated data to the rest according to this probability. This behavior can affect the performance of the whole network in an adverse manner especially in cases where the cost is high. To overcome this problem, we propose an adaptive reputation protocol which enables the agents to evaluate the behavior of their neighbors over time and select the most reputable subset of neighbors to share genuine information with. We provide an extensive simulation-based analysis to compare the performance of the proposed method with several other distributed learning strategies. The results show that the proposed method outperforms the other learning strategies and enables the network to have a superior performance especially when the cost of sharing genuine information is high