MPFL: A Decentralised Federated Learning Framework Based on Multi-Population Genetic Algorithm
Authors: Wenqi Ding, Yuanchao Liu, Zhongjie Wang, Zheng Chu


Reviewer paper summaries: This paper introduces a decentralized Federated Learning approach that combines blockchain technology and multi-population genetic algorithms. The goal of this proposed framework MPFL is to improve performance while enhancing robustness of decentralized FL. The MPFL utilizes a hierarchical FL structure by organizing clients into distinct populations and employing hierarchical aggregation to boost robustness and efficiency. Key features include four GA operators and a committee consensus mechanism, which consists of a voting and election strategy.
Summary of strengths: 1.	The problem addressed in this paper is practical and highly relevant to real-world concerns in decentralized FL frameworks.
2.	The paper successfully integrates various aspects of Genetic Algorithms (GAs) into the decentralized federated learning framework.
3.	The paper is well-organized and clearly presented, making it easy for readers to follow and understand.
Summary of weaknesses: 1.	Since the number of FLOPs spent to train a model in one epoch may not always be a reliable measure of an algorithm's actual running time, I believe the authors should also include wall clock time in the efficiency comparison.
2.	The authors should consider removing the mutation operator for the ablation study since adding noise during training is a fundamental operation for enhancing robustness.
3.	From the efficiency comparison in the paper, Hierarchical FL has lower computation and communication costs compared to MPFL. However, the paper does not present how the effectiveness of Hierarchical FL compares to that of MPFL, making it unclear what the actual advantages of MPFL are.
4.	The baselines compared in the experiment are as recent as 2018, which is somewhat outdated for the well-studied field of decentralized federated learning. Additionally, the models and datasets used are too simple, making it difficult to effectively evaluate the performance of this framework.
Comments, suggestions and typos: Weaknesses.
Overall assessment scores: 2