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Basic reporting
I have reviewed the comments that have been provided by the reviewer, the author has addressed all the comments, and the paper is a very good standard for publication in Peer J computer science. The paper has contributed to science, knowledge, and the research community.
Experimental design
The paper has provided the research community with very interesting information available in the federation learning related to IoT in smart cities and their applications. All comments have been addressed by the author.
Validity of the findings
I am happy with the findings and outcome of this paper and it will help the researchers.
[# PeerJ Staff Note - this decision was reviewed and approved by Miriam Leeser, a PeerJ Section Editor covering this Section #]
I have reviewed the responses provided by the reviewer and find them satisfactory. I hereby accept the explanations and answers given
I have reviewed the responses provided by the reviewer and find them satisfactory. I hereby accept the explanations and answers given
I have reviewed the responses provided by the reviewer and find them satisfactory. I hereby accept the explanations and answers given
The authors addressed all my concerns. I agree to accept the paper.
N/A
N/A
The paper has contributed toward the Federation learning with IoT smart city application so, I am happy to take it for another round and give a chance to authors to address all the comments provided by the reviewers.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
**Language Note:** The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at copyediting@peerj.com for pricing (be sure to provide your manuscript number and title). Alternatively, you should make your own arrangements to improve the language quality and provide details in your response letter. – PeerJ Staff
The abstract paper "Federated Learning with IoT: Challenges and Solutions" discusses the need of real-time monitoring utilizing IoT data to solve privacy concerns and weaknesses in commercial financial models. The article discusses the issues IoT device manufacturers and consumers encounter when incorporating AI into real-world applications that require centralized data collecting and processing. Federated Learning (FL) allows distributed IoT devices to train AI models without sharing sensitive data. The study examines FL-IoT safety and FL applications in various IoT sectors. It stresses the necessity for more study to handle FL integration with IoT and encrypted data transmission.
No comments
What are the contributions to this paper?
No comments
I noticed that grammatical errors and a lack of clarity in expression hinder the overall readability of the manuscript. To ensure the comprehensibility of your findings, I kindly suggest a careful proofreading and editing process to rectify these language-related issues.
Figures are blurry and not readable.
Secondly, it is challenging for me to discern whether your manuscript falls within the realms of traditional, systematic, or meta-analysis literature review. It would greatly assist both the readers and reviewers if you could explicitly specify the type of survey you intend to present. This clarification would undoubtedly facilitate a more accurate assessment of your manuscript.
Not applicable in its current form.
After a comprehensive evaluation of your submission, I am inclined to withhold acceptance of the article in its current form. Nevertheless, I encourage you to consider revising and resubmitting your work with a more defined structure and precise research questions. These refinements will undoubtedly contribute to the provision of up-to-date and valuable information for your readers.
The paper discusses the challenges and solutions related to the integration of federated learning (FL) and the Internet of Things (IoT). It highlights the importance of FL in protecting user privacy, improving model performance, enabling flexible scalability, and enhancing learning quality in IoT networks. The paper also mentions specific challenges in FL-IoT, such as resource management, updates aggregation, privacy protection, and security issues, learning and communication issues, standardized specifications, and AI functions deployed on IoT sensors.
The review aims to provide insights into the challenges faced during the integration of FL and IoT, as well as potential solutions and opportunities.
This paper meets the standards.
The conclusion section does not explicitly mention unresolved questions, gaps, or future directions. It primarily focuses on summarizing the challenges in federated learning with the Internet of Things (IoT) and the potential solutions that have been discussed in the study.
There’s no explicit point in the text to Table I and Table II, so it may be difficult for readers to refer to these tables.
Some typos should be revised for better readability.
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