FedDSS: A data-similarity approach for client selection in horizontal federated learning

Background and objective: Federated learning (FL) is an emerging distributed learning framework allowing multiple clients (hospitals, institutions, smart devices, etc.) to collaboratively train a centralized machine learning model without disclosing personal data. It has the potential to address several healthcare challenges, including a lack of training data, data privacy, and security concerns. However, model learning under FL is affected by non-i.i.d. data, leading to severe model divergence and reduced performance due to the varying client's data distributions. To address this problem, we propose FedDSS, Federated Data Similarity Selection, a framework that uses a data-similarity approach to select clients, without compromising client data privacy. Methods: FedDSS comprises a statistical-based data similarity metric, a N-similar-neighbor network, and a network-based selection strategy. We assessed FedDSS' performance against FedAvg's in i.i.d. and non-i.i.d. settings with two public pediatric sepsis datasets (PICD and MIMICIII). Selection fairness was measured using entropy. . Simulations were repeated five times to evaluate average loss, true positive rate (TPR), and entropy. . Results: In i.i.d setting on PICD, FedDSS achieved a higher TPR starting from the 9th round and surpassing 0.6 three rounds earlier than FedAvg. On MIMICIII, FedDSS's loss decreases significantly from the 13th round, with TPR > 0.8 by the 2nd round, two rounds ahead of FedAvg (at the 4th round). In the non-i.i.d. setting, FedDSS achieved TPR > 0.7 by the 4th and > 0.8 by the 7th round, earlier than FedAvg (at the 5th and 11th rounds). In both settings, FedDSS showed reasonable fairness ( entropy of 2.2 and 2.1). Conclusion: We demonstrated that FedDSS contributes to improved learning in FL by achieving faster convergence, reaching the desired TPR with fewer communication rounds, and potentially enhancing sepsis prediction (TPR) over FedAvg.