Predictive Maintenance for Optical Networks in Robust Collaborative Learning  

1 INTRODUCTION . Optical fiber networks compose the core of telecommunication infrastructure today due to their high capacity of data transmission . Optical networks rely on fully functional hardware components that run under optimal conditions . In order to reduce the risk of unplanned network interruption and service outage , it is important to predict the degradation of hardware network components correctly using analyzing tools and techniques , by which the maintenance budget and resources are allocated efficiently and timely . Due to the great benefits in industry , global predictive maintenance market is expected to reach more than $ 13 billion by 2026 ( ReportLinker , 2021 ; Simon , 2021 ) . Machine learning ( ML ) based prediction is an emerging method to improve the accuracy of predictive maintenance in manufacturing industry and communication networks . An ML model is trained by the historical data of hardware failure and then the upcoming maintenance is predicted by realtime data gathered through measurement at the edge . ML techniques can be useful if a sufficiently large , diverse , and realistic set of training data exists . Since an ML model relies so heavily on good training data , the availability of such datasets is a crucial requirement for this approach . However , it is challenging to develop a high-precision ML model for predictive maintenance mainly due to the lack of training data . The hardware failures or maintenance events do not occur frequently so that it takes time until good and meaningful training data are collected through the network . Hence , an ML model is often trained using the accelerated aging test results ( e.g . a life cycle under the extreme temperature or the over-powered condition ) that are conducted by hardware manufacturers . Since the components of network equipment are usually produced by small and medium-sized companies , such an ML model is trained based on the limited amount of data that are owned by each manufacturer . This situation can be relieved , if the training dataset can be aggregated from multiple vendors and consolidated in a central location . Since collaborative learning allows to train a model on larger datasets rather than the dataset available in a single vendor , a higher quality and more accurate ML model can be built . However , such collaboration is not straightforward in reality since vendors are not willing to share their training data with external companies . Aging test data are often companyconfidential and trade secret . Moreover , sharing data with foreign companies may be prohibited by privacy protection regulations in their home countries . Federated Learning Federated learning ( FL ) is a framework of enabling distributed parties to work together to train machine learning models without sharing the underlying data or trusting any of the individual participants ( Bonawitz et al. , 2017b ) . FL can be used to build an ML model from various companies for the purpose of predicting the failures , repairs , or maintenance of network systems . With the FL technique , the training data is not required to be centralized , but can instead remains with the data owners . Each vendor trains an ML model on their private data and using their own hardware . These models are then aggregated by a central server ( e.g . a network operator ) to build a unified global model that has learned from the private data of every vendor without ever directly accessing it . Hence , confidential training data ( e.g . aging test results of products ) are not visible to a server , nor other competitive vendors . Secure aggregation Secure aggregation in FL is a cryptographic protocol that enables each vendor to submit a local model securely and a server learns nothing but the sum of the local models . A secure aggregation method for mobile networks was presented in Bonawitz et al . ( 2017b ) and Bell et al . ( 2020 ) . This method relies on a pairwise secret exchange and Shamir ’ s t-out-of-n secret sharing scheme , focusing on the setting of mobile devices where communication is extremely expensive , and dropouts are common . There is a rich literature exploring secure aggregation in both the single-server setting ( via additive masking Bonawitz et al . ( 2016 ) , via threshold homomorphic encryption ( HE ) Halevi et al . ( 2011 ) , and via generic secure multi-party computation ( MPC ) Burkhart et al . ( 2010 ) ) as well as in the multiple non-colluding servers setting ( Corrigan-Gibbs & Boneh , 2017 ) . For instance , one can perform all computations using a fully homomorphic encryption scheme resulting in low communication but very high computation , or using classical MPC techniques with more communication but less computation . Other works use a hybrid of both and thus enjoy further improvement in performance ( Juvekar et al. , 2018 ; Mishra et al. , 2020 ) . Nevertheless , it is still an open question how to construct a secure and robust aggregation protocol that addresses all the challenges . Our contribution In this paper , we propose a secure and robust collaborative learning method using cross-vendor datasets for predictive maintenance in optical networks . Each vendor builds a local model using its own training dataset and uploads it to the server . The private dataset remains in the vendor ’ s domain and is never exposed to other companies . A server builds a global ML model by aggregating local ML models iteratively and averaging them to form an updated global model proportional to the size of dataset . Using the global model , the potential risk of hardware failure and corresponding maintenance events are predicted and the necessary resources are proactively prepared to run optical networks without disruption . In our framework , a secure aggregation protocol is tolerant to the malicious behavior of participants in a honest-majority model ; that is , a server and majority of vendors are assumed to be honest yet some may be malicious or unreliable . Compared to the original FL , the local models are not many , and the dropouts are very rare in our framework . Furthermore , an updated global model is not shared with vendors . The reason is that , while a global model is a valuable asset to the network management , it is not really beneficial to the vendors . Instead , each vendor receives the personalized maintenance report which contains the discrepancy between its local model and the global model , which is useful to improve the quality of products in the future . Fig . 1 shows that an example of the ML-based predictive maintenance process in FL under the assumption that a single vendor behaves maliciously . Related work In Bonawitz et al . ( 2017a ) , a practical secure aggregation technique in an FL setting was proposed over large mobile networks . Such framework does not fit for our use case due to multiple reasons . Firstly , in our use case , a global model is not shared with data owners ( vendors ) . Each vendor gets benefit by receiving an individual maintenance result ( e.g . the difference between the prediction and the real failure ) after the global model is deployed and hardware degradation is predicted . Secondly , the scalability is not important since the number of vendors are not very large and dropouts are expected to be rare . On the other hand , secure aggregation is critical since the disclosure of the private training dataset may give negative impact on the data owner ’ s business . Another interesting work on collaborative predictive maintenance was presented in Mohr et al . ( 2021 ) , where a combination of blockchain and federated learning techniques was applied . We apply a multi-party computation technique for data privacy since it is more suitable for our use case . More recently , in Zheng et al . ( 2021 ) , an end-to-end platform for collaborative learning using MPC is proposed . Though it is an interesting approach , it is unlikely that this platform can be applied to our use case since the collaborative learning through the use of release policies and auditing is not preferable to the predictive maintenance . The rest of this paper is structured as follows : The use case and methods of ML-based predictive maintenance are presented . Then , the threat scenarios and defending methods are presented . Our experimental results are presented and the conclusion is given . 2 ML-BASED PREDICTIVE MAINTENANCE . 2.1 USE CASE : OPTICAL TRANSMITTER DEGRADATION PREDICTION . Semiconductor lasers are considered as one of the most commonly used optical transmitters for optical communication system thanks to their high efficiency , low cost , and compactness . They have been rapidly evolved to meet the requirements of the next generation optical network in terms of high speed , low power consumption etc . However , during operation the performance of the laser can be adversely impacted by several factors such as contamination , facet oxidation etc . Such factors are hard to predict and cause the laser degradation and failure , and thereby resulting in optical network disruption and high maintenance costs . Therefore , it is highly required to predict the degradation of the semiconductor laser device after its deployment in optical communication system in order to enhance the system reliability and minimize the downtime costs . ML techniques could provide a great potential to tackle the laser degradation prediction problem ( Abdelli et al. , 2020 ) . The development of such prognostic methods requires the availability of run-to-failure data sets modelling both the normal operation behavior and the degradation process under different operating conditions . However , such data is often unavailable due the scarcity of the failures during the system operation and the long time required to monitor the device up failing and then generating the reliability data . That ’ s why accelerated aging tests are often adopted to collect run-to-failure data in shorter time by speeding up the device degradation by applying accelerated stress conditions resulting in the same degradation process leading to failure ( Celaya et al. , 2011 ) . However , the burn-in aging tests are carried out just for few devices due to the high costs of performing such tests . Hence , the amount of the run-to-failure data that can be derived from such tests , might be small , which can adversely affect the performance of ML model ( Abdelli et al. , 2021 ) . Therefore , a FL approach is considered as a promising candidate to address the aforementioned problem , whereby different semiconductor laser manufacturers ( i.e vendors ) collaborate with their small local dataset , stored at their premise , in order to build an accurate and reliable global laser degradation prediction model with good generalization and robustness capabilities . Note that the semiconductor laser manufacturers might have different types of laser devices with various characteristics leading to different degradation trends , and that the data owned by each vendor is derived from aging tests conducted under different operating conditions . State that the global model is run in a server hosted by an optical network operator owning the infrastructure in which the semiconductor lasers manufactured by the different vendors are deployed . We consider a FL system composed of a server and N clients ( i.e vendors ) that collaboratively train a global model using the FedAvg algorithm ( McMahan et al. , 2017 ) . The clients send securely the local model weight updates to the server using MPC . The autoencoder based on gated recurrent unit ( GRU ) is used as global model to solve the task of semiconductor laser degradation prediction . A convolutional autoencoder implemented at the server is adopted as an anomaly detection method to detect the anomalous weights sent by the malicious clients .

Summary: This paper uses federated machine learning for predictive maintenance on optical networks. Federated learning provides a number of advantages, including security, privacy, and accuracy. The accuracy claims are motivated by having a broader set of failure examples to draw from, addressing a known issue in predictive maintenance. Privacy and security claims are important in predictive maintenance, but were less novel or well supported - they felt more like off the shelf methods applied in a new domain.   Their primary comparison was against a global model.