Federated Learning (FL) represents a paradigm shift in machine learning, allowing collaborative model training while keeping data localized. This approach is particularly pertinent in the Industrial Internet of Things (IIoT) context, where data privacy, security, and efficient utilization of distributed resources are paramount. The essence of FL in IIoT lies in its ability to learn from diverse, distributed data sources without requiring central data storage, thus enhancing privacy and reducing communication overheads. However, despite its potential, several challenges impede the widespread adoption of FL in IIoT, notably in ensuring interpretability and robustness. This article focuses on enabling trustworthy FL in IIoT by bridging the gap between interpretability and robustness, which is crucial for enhancing trust, improving decision-making, and ensuring compliance with regulations. Moreover, the design strategies summarized in this article ensure that FL systems in IIoT are transparent and reliable, vital in industrial settings where decisions have significant safety and economic impacts. The case studies in the IIoT environment driven by trustworthy FL models are provided, wherein the practical insights of trustworthy communications between IIoT systems and their end users are highlighted.

The industrial Internet of Things (IIoT) under Industry 4.0 heralds an era of interconnected smart devices where data-driven insights and machine learning (ML) fuse to revolutionize manufacturing. A noteworthy development in IIoT is the integration of federated learning (FL), which addresses data privacy and security among devices. FL enables edge sensors, also known as peripheral intelligence units (PIUs) to learn and adapt using their data locally, without explicit sharing of confidential data, to facilitate a collaborative yet confidential learning process. However, the lower memory footprint and computational power of PIUs inherently require deep neural network (DNN) models that have a very compact size. Model compression techniques such as pruning can be used to reduce the size of DNN models by removing unnecessary connections that have little impact on the model's performance, thus making the models more suitable for the limited resources of PIUs. Targeting the notion of compact yet robust DNN models, we propose the integration of iterative magnitude pruning (IMP) of the DNN model being trained in an over-the-air FL (OTA-FL) environment for IIoT. We provide a tutorial overview and also present a case study of the effectiveness of IMP in OTA-FL for an IIoT environment. Finally, we present future directions for enhancing and optimizing these deep compression techniques further, aiming to push the boundaries of IIoT capabilities in acquiring compact yet robust and high-performing DNN models.

The graph represents a network of 1,371 Twitter users whose recent tweets contained "iiot machinelearning", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 3/9/2023 5:00:36 PM. The network was obtained from Twitter on Friday, 10 March 2023 at 12:23 UTC. The tweets in the network were tweeted over the 1827-day, 0-hour, 27-minute period from Friday, 09 March 2018 at 00:30 UTC to Friday, 10 March 2023 at 00:58 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph represents a network of 1,389 Twitter users whose recent tweets contained "iiot machinelearning", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 2/9/2023 5:00:35 PM. The network was obtained from Twitter on Friday, 10 February 2023 at 12:24 UTC. The tweets in the network were tweeted over the 1474-day, 5-hour, 23-minute period from Sunday, 27 January 2019 at 19:36 UTC to Friday, 10 February 2023 at 01:00 UTC. There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above. The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph represents a network of 1,479 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 16 December 2022 at 11:45 UTC. The requested start date was Friday, 16 December 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 6-hour, 56-minute period from Tuesday, 13 December 2022 at 18:03 UTC to Friday, 16 December 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 1,935 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 09 December 2022 at 11:48 UTC. The requested start date was Friday, 09 December 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 3-day, 1-hour, 52-minute period from Monday, 05 December 2022 at 23:07 UTC to Friday, 09 December 2022 at 00:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 1,758 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 09 December 2022 at 12:09 UTC. The requested start date was Friday, 09 December 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 3-day, 12-hour, 10-minute period from Monday, 05 December 2022 at 12:48 UTC to Friday, 09 December 2022 at 00:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 1,690 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 21 June 2022 at 22:09 UTC. The requested start date was Tuesday, 21 June 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 8-hour, 11-minute period from Sunday, 19 June 2022 at 15:49 UTC to Tuesday, 21 June 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 1,351 Twitter users whose tweets in the requested range contained "iiot machinelearning", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 03 June 2022 at 11:23 UTC. The requested start date was Friday, 03 June 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 16-hour, 11-minute period from Tuesday, 31 May 2022 at 07:48 UTC to Friday, 03 June 2022 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Manufacturing execution systems (MES) have undergone many transformations in the past several years--from simple point solutions to comprehensive shop floor systems that are now mission-critical to manufacturing operations. As I mentioned in part one of this series, the union between MES and smart manufacturing technology gives manufacturing enterprises access to new, advanced capabilities. From increased operating margins to decreased costs, manufacturers can leverage this smart combination and find themselves with a significant competitive edge globally. In part one and part two of this three-part series, we looked at four significant new aspects of MES: mobility and the use of artificial intelligence (AI), track-and-trace database capabilities and the use of many applications. The IIoT is becoming synonymous with smart manufacturing.