Fair Distributed Cooperative Bandit Learning on Networks for Intelligent
  Internet of Things Systems (Technical Report)
Ziqun Chen Kechao Cai Jinbei Zhang Zhigang Yu
In intelligent Internet of Things (IoT) systems, edge servers within a
network exchange information with their neighbors and collect data from sensors
to complete delivered tasks. In this paper, we propose a multiplayer
multi-armed bandit model for intelligent IoT systems to facilitate data
collection and incorporate fairness considerations. In our model, we establish
an effective communication protocol that helps servers cooperate with their
neighbors. Then we design a distributed cooperative bandit algorithm, DC-ULCB,
enabling servers to collaboratively select sensors to maximize data rates while
maintaining fairness in their choices. We conduct an analysis of the reward
regret and fairness regret of DC-ULCB, and prove that both regrets have
logarithmic instance-dependent upper bounds. Additionally, through extensive
simulations, we validate that DC-ULCB outperforms existing algorithms in
maximizing reward and ensuring fairness.