How does the FedMCCS approach improve fairness and heterogeneity in federated learning for resource allocation?

FedMCCS improves fairness and heterogeneity by using multi-criteria client selection, which makes federated training fairness-aware and heterogeneity-aware. The book summarizes FedMCCS in the context of IoT resource allocation as achieving this via a multicriteria client selection approach.

According to the book's Table 2.4, FedMCCS is an FL-based approach for resource allocation and load balancing in IoT networks. Its technique is described as "FL + Multi-criteria client selection," and the reported benefit is "fairness and heterogeneity-aware training" (reference [62]). The surrounding text also notes that decentralized FL models often suffer from slower convergence, lower accuracy, straggler clients, and data heterogeneity, and FedMCCS is cited in that discussion. The source does not provide the detailed internal criteria or algorithm used for client selection; it only characterizes FedMCCS as a multicriteria client selection model that addresses fairness and heterogeneity.