Predictive Maintenance for Optical Networks in Robust Collaborative Learning  
Authors: Khouloud Abdelli
JOO YEON CHO
Venue: ICLR 2022 Submitted (2022)
Keywords: predictive maintenance
federated learning
machine learning
anomaly detection
multi-party computation
autoencoder

Abstract: Machine learning (ML) has recently emerged as a powerful tool to enhance the proactive optical network maintenance and thereby, improve network reliability and operational efficiency, and reduce unplanned downtime and maintenance costs. However, it is challenging to develop an accurate and reliable ML based prognostic models due mainly to the unavailability of sufficient amount of training data since the device failure does not occur often in optical networks. Federated learning (FL) is a promising candidate to tackle the aforementioned challenge by enabling the development of a global ML model using datasets owned by many vendors without revealing their business-confidential  data. While FL greatly enhances the data privacy, a global model can be strongly affected by a malicious local model. We propose a robust collaborative learning framework for predictive maintenance on cross-vendor in a dishonest setting. Our experiments confirm that a global ML model can be accurately built with sensitive datasets in federated learning even when a subset of vendors behave dishonestly.


Under review as a conference paper at ICLR 2022
PREDICTIVE MAINTENANCE FOR OPTICAL NET- WORKS IN ROBUST COLLABORATIVE LEARNING
Anonymous authors Paper under double-blind review
ABSTRACT
Machine learning (ML) has recently emerged powerful tool to enhance the proactive optical network maintenance and thereby, improve network reliabil- ity and operational efficiency, and reduce unplanned downtime and maintenance costs. However, it is challenging to develop an accurate and reliable ML based prognostic models due mainly the unavailability of sufficient amount of training data since the device failure does not occur often in optical networks. Federated learning (FI.) is a promising candidate to tackle the aforementioned challenge by enabling the development of a global MI. model using datasets owned by many vendors without revealing their business-confidentie data. While FL greatly en- hances the data privacy, global model can be strongly affected by malicious local model. We propose a robust collaborative learning framework for predic- tive maintenance cross-vendor in dishonest setting. Our experiments confirm that a global ML model can be accurately built with sensitive datasets in federated learning even when subset of vendors behave dishonestly.
INTRODUCTION
Optical fiber networks compose the core of telccommunication 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, 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 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 predic- tive 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 real- time data gathered through measurement the edge. ML techniques can be useful if 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 crucial requirement for this approach.
However, it is challenging to develop 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- weee-poeeeed condition) that are conducted by hardware manufactur- ers. Since the components of network equipment are usually produced by small and medium-sizee companies, such an MI. 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 single vendor, higher quality and more accurate ML model can be built. However, such collaboration is not straightforward in reality since vendors are not willing share their training data with external companies. Aging test data are often company- confidential and trade secret. Moreover, sharing data with foreign companies may be prohibited by privacy protection regulations in their home countries.

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Federated Learning Federated learning (FL) is 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 central server (e.g. a network operator) to build a unified global model that has learnd 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 ven- dor to submit local model securely and 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 [Blll al. (2020). This method relies on pairwise secret exchange and Shamir's 's t-out-of-n secret shar- ing scheme, focusing on the setting of mobile devices where communication is extremely expensive, and dropouts are common.
There is 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) Burkhrttt et al. (2010)) as well as in the mul- Boneh] 2017). For instance, one can perform
tiple non-colluding servers setting (Corrigan-Gibbs 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 secure and robust aggregation protocol that addresses all the challenges.
Our contribution In this paper, we propose secure and robust collaborative learning method using cross-vendor datasets for predictive maintenance 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 global ML model by aggregating local ML models iteratively and averaging them form 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, secure aggregation protocol is tolerant to the malicious behavior of participants in a honest- conest-majority model; that is, a server and majority of vendors are assumed to be honest yet some may be malicious or unreliable. Compared 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 global model is 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. [ 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 In/Bonawitz et al. 7a), 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, 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 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 s business.
Another interesting work collaborative predictive maintenance was presented in Mohr et al. (202T), where a combination of blockchain and federated learning techniques was applied. We apply a multi-party computation technique for data privacy since it more suitable for our use case.
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Poisoned data
Vendor
vender
Optical Network
Predictive
Local model
Secure Aggregation
Vendor 2
Model Depleyment
Update Model
Secure Distribution
Field test results
Figure 1: ML-based predictive maintenance process in a dishonest setting
More recently, in Zheng al. 22021) end-to-end platform for collaborative learning using MPC proposed. Though it is an interesting approach, it unlikely that this platform can be applied to use 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 maintenance are presented. Then, the threat scenarios and defending methods are presented. Our experimental results are presented and the conclusion is given.
and methods of ML-based predictive
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 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 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 monitor the device up failing and then generating the reliability data. That's why accelerated aging tests are often adopted to collect cun-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 per- forming such tests. Hence, the amount of the run-to-failure data that can be derived from such tests, might small, which can adversely affect the performance MI. model (Abdelli et al., 2021). Therefore, a FL approach is considered problem, whereby different semiconductor laser manufacturers (i.e vendors) collaborate with their small local dataset, stored at their premise, in order to build accurate and reliable global laser degradation prediction model with good generalization and robustness capabilities.
promising candidate to address the aforementioned
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 ven- dor is derived from aging tests conducted under different operating conditions. State that the global
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model is run in server hosted by optical network operator owning the infrastructure in which the semiconductor lasers manufactured by the different vendors are deployed.
We consider FL system composed of 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 the server using MPC. The autoencoder based on gated recurrent unit (GRU) is used as global model to solve the task semiconductor laser degradation prediction. A convolutional autoencoder implemented at the server adopted as anomaly detection method to detect the anomalous weights sent by the malicious clients.
2.2 METHOSS
Autoencoder An autoencoder (AE) is a type of artificial neural network seeking to learn a com- pressed representation of an input in unsupervised manner <Kramer [991). An AE is composed of two sub-models namely the encoder and the decoder. The encoder is used to compress an in- put X into lower-dimensional encoding (i.e. latent-space representation) Z through non-linear transformation, which is expressed as follows:
Z = f(WX + b);
(1)
where W and b denote the weight and bias matrices of the encoder and f represents the activation function of the encoder.
The decoder attempts reconstruct the output X given the representation Z via nonlinear trans- formation, which it is formulated as follows:
X o (W'XX) X +)),
where W and represent the weight and the bias matrices of the decoder and g denotes the activa- tion function of the decoder.
The AE is trained by minimizing the reconstruction error between the output X and the input X, which is the loss function L(O), typically the mean square error (MSE), defined as:
L(O) Σ X X11
where 0 = (W,6 b, W .b) denotes the set of the parameters to be optimized.
Gated Recurrent Unit (GRU) GRU recently proposed by (Cho et al. 2014) to solve the gradient vanishing problem Cho et al. (2144) is an improved version of standard recurrent neural networks (RNNs), used to process sequential data and to capture long-term dependencies. The typical struc- ture of GRU contains two gates namely reset and update gates, controlling the flow of the infor- mation. The update gate regulates the information that flows into the memory, while the reset gate controls the information flowing out the memory. The GRU cell is updated at each time step by applying the following equations:
24 oW, x + W. h(1-1) + by)
(4)
T oWW - + W, h(t-1) + by)
(5)
he ann(W, xt + Wn (r, h1z-1)) + bb)
b o h(r-) + (1 21) h4
where 2, denotes the update gate, Tt represents the reset gate, H, is the input vector, h the output vector, W and represent the weight and the bias matrices respectively. a is the gate activation function and tanh represents the output activation function. The '.' operator denotes matrix mul- tiplication, the "o" operator represents the dot product.
2.3 ANOMALY DETECTION
AE has been widely used for anomaly detection by adopting the reconstruction eттоr as anomaly score. It is trained with only normal data representing the normal behavior. After training, AE will reconstruct the normal instances very well, while it will fail to reproduce the anomalous obser- vations by yielding high reconstruction errors. The process of the classification of instance
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observation anomalous/normal is shown in Alg. I If the calculated anomaly score is higher than a set threshold 8, the instance classified as "anomalous", else it is assigned as "normal". 0 is a hyperparameter optimized based on the number of true positives and false positives.
Algorithm Autoencoder-based anomaly detection algorithm
Input Normal dataset X, Anomalous dataset x ; 1,... N, threshold 0
Output Reconstruction error || æ||
f.g < train an autoencoder using the normal dataset X for i 1.... .N do
reconstruction error(i) +- ||x(0) fx(
reconstruction error(i) 0 then x(0) is anomalous.
else
z(() is normal.
end end for
3 ROBUST COLLABORATIVE LEARNING
We consider training an ML model a federated learning setting, wherein each vendor maintains private dataset of its own hardware. A global ML model is trained under the coordination of central server based upon multiple local models that are provided by different vendors. server can get only sum of the local models and does model, the maintenance events in optical networks are predicted and the corresponding materials are prepared accordingly.
local models individually. Based on the global
3.1 THREATS
An important challenge in FL is to prevent server other vendors from reconstructing the private data of any vendor while collaborating at any circumstances. While a secure aggregation protocol in FL addresses the strong privacy of the data of the vendors, the FL framework creates new attack surface during the model training process. Since the vendors have full control over local training processes, they may submit arbitrary updates to change the global model without being detected. Among the broad range of attacks on FI., following attacks are most relevant to our use case.
Model inversion attack An attacker can intercept the updated local models and extract the private training data from the models. For example, in FFedrikkon al. (2015), authors demonstrated model inversion attack that could extract images from face recognition system, which look suspiciously similar images from the underlying training data. The model inversion attacks can be mitigated by applying differential privacy techniques; adding noise to the local models before sending them to the server. However, such noise will degrade the overall model performance, which not preferable
case.
Local model poisoning attack This attack injects poisoned instances into the training data, di- rectly manipulates model updates during the aggregation protocol. Instead of locally trained models, these Byzantine vendors may upload the poisoned local models which are highly deviated from the global model. As result, the attacker can tamper with the weights of the global model or inject backdoor into it, misclassifying specific inputs into the target class as intended by the attacker.
Active corruption secure aggregation protocol enables each vendor to submit a local model securely and the server learns nothing but the sum of the local models. In this attack, some corrupted vendors may arbitrarily deviate from the secure aggregation protocol. Some vendors do not follow the protocol honestly and provide wrong values to the server other vendors.
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Server
%
Pd
fits,
Pa
52
Pao
fr.t
Pv
Vendor (ibe)
Vendar
Vendor N
100
Sp (P Sy (1
Piu
Pau
Syi
Phil
Dus
(Fm Pay.. Pha)
Pzz... Pxal
Paaw Pau... Pau
Figure 2: Secure collaborative learning using Secret Sharing in FL
3.2 DEFENSES
We present two defending methods against attacks in FL: MPC-based secure aggregation against the model inversion attack and anomaly detection against the model poisoning attack and the active corruption attack.
MPC-based Secure aggregation Suppose that the server and vendors (clients) behave honestly but curiously (semi-honest model). That is, participants follow the protocol exactly instructed but also try retrieve the private data of other vendors, if possible. Under this scenario, a simple n-out-of-n additive secret sharing scheme can be used to prevent the model inversion attack as well as keep the privacy of local models.
Suppose N is the number of clients and each client has its own local model fa where IsiS N. The client generates random linear mask 5; and sends fi + s; to the server. Also, the client i in such way that 8; = Eÿ= Pij.
divides 8; into N additive shares, -pil:-..,Pia of 8; is similar those of shares. These N shares distributed other clients in such way that each client receives unique share out of N shares. In result, the client receives (pi..... PNvs) }- Finally, the client sends the sum of the shares Σ; Pşi the server. This process is repeated for all clients.
Pij. Note the size
By aggregating one-time padded local models and the sum of the shares, the server can calculate the sum of the local models follows:
N
N
N
N 1=1
N
N
(f++tt)
Pij
f
(8)
1=1
j=1
i=1
A pseudo code of the secure aggregation protocol is given in Appendix A.1 secure collaborative learning procedure is shown in Fig. 2
An overview of the
Autoencoder based anomaly detection The autoencoder is trained with a dataset D (wa, w²... W. incorporating the model weights sent by trusted clients (i.e normal weights) and stored at the server. The dimensionality of the model weight w is reduced to produce a low- dimensional input order reduce the computational complexity due the high dimension of the model weight. The generated input is fed then to the autoencoder for training, whereby the encoder compresses the input into lower-dimensional latent vector which then reconstructed by the de- coder. After the training phase, the autoencoder is able to recognize the normal weights and mark any weight that deviates from the data seen during the training as an anomaly. The reconstruction emror between the input weight and the reconstructed weight is used as an anomaly score. If the anomaly score exceeds a pre-defined threshold, the weight is recognized as anomalous potentially sent by malicious client, and thereby it is removed and not considered for the update of the global model. The threshold is optimized in order to improve the detection capability the autoencoder for different poisoning model attacks.

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4 EXPERIMENTS
4.1 EXPERIMENTAL DATA
The experimental data is derived from various accelerated aging tests performed for different semi- conductor laser devices operating under several operating conditions and carried out under high temperature 2 50CC) strongly increase the laser degradation and thereby speed up the device failure. The output power (i.e degradation parameter) is monitored under constant current, until 15,000 The failure or degradation criteria of the device defined as the decrease of the output power by 20% of its initial value. Few devices are degraded or failed either during the aging test or after the end of the test. In total, a dataset of 6, 6,786 samples incorporating the sequences of monitored output measurements is built. We assign each sample the state of the device (normal degraded (i.e anomalous ) by applying the aforementioned failure criteria. The said data is then normalized and randomly divided into training data (comprising of 80% of the samples) and a test dataset (the remaining 20% for testing). The training dataset incorporates only samples of normal devices, whereas the test dataset includes samples of both normal and degraded devices. The training data is split then into N=10 clients with different parts of 450, 500, 554, 700, 382, 520, 450, 445, 300, and 380 respectively, leading heterogeneous federated setting.
4.2 GLOBAL MODEL
Given that the data is highly unbalanced due to the small number of failed devices, adopting pervised methods can be unfeasible due to the lack of adequate number of normal and fault data. Therefore, an unsupervised method namely the GRU-based autoencoder (GRU-AE) is used global model. GRU is adopted to capture the laser degradation trend in sequential input. The GRU-AE is trained with normal data underlying the normal behavior of the laser device, and tested with obser- vations of normal and failed devices. Note that the input sequences fed to the GRU-AE include the output power measurements collected only till 5,000 h in order train the model to early predict the degradation and that it is tested with sequences of normal and degraded devices failed after 5,000 evaluate the early prediction capability. The input of the global model consists of a 6-1ength sequence of historical output power measurements combined with the operating conditions features namely the temperature and the current, impacting the degradation trend. The architecture of global model is composed of two GRU layers containing each 64 cells. Rectified Linear Unit (Rel.U) is selected as an activation function for the hidden layers of the model. The training of the global model is carried out in iterative process as follows:
The server distributes the global model w to N clients.
Each client trains the model locally using its local data Dx, and updates the weight w for epochs of Adam with mini-batch size of B to compute w (r+1))
The server securely aggregates each client's w(i+1) using MPC.
An autoencoder- based anomaly detection method used to detect anomalous weights sent by the clients.
The update of the global model w is computed by weighted averaging of only normal weights.
The above-described process is repeated for multiple communication rounds Nround (e.g. number of aggregation) to improve the performance of the global model. For our experiments, a, 3 and Nround are set to 10, 16 and 100 respectively.
4.3 ANOMALOUS WEIGHT DETECTION METHOD
A convolutional autoencoder implemented at the server is used to identify the anomalous weights and thereby detect the potentially malicious clients. The model contains encoder and decoder sub-model with 5 layers. The encoder takes as an input a vector of length 64. It encodes the input into low dimensional features through series of 2 convolutional layers containing 64 and 32 filters of size x 1 with a stride of 2 and 1, respectively. The decoder is inversely symmetric to the encoder part. It consists of 3 transposed convolutional layers used to up-sample the feature maps. The last
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1.0
Metric value
0.8
Precision
0.6
Recall
F1 score
0.4
0.005
0.010
0.015
0.020
Figure 3: The optimal threshold selection based on the precision, recall and F1 score scores yielded by the GRU-based autoencoder
transposed convolutional layer with filter of size and stride of 1 is used generate the output. ReLU selected as an activation function for the hidden layers of the model. The loss function is set the MSE, which is adjusted by using the Adam optimizer.
4.4 EXPERIMENTAL RESULTS
Prediction Capability Evaluation We evaluate the traditional centralized approach by applying the GRU-AE to a centralized aggregated data containing the datasets all the clients. The degrada- tion prediction capability of the GRU-AE is optimized by selecting optimal threshold A ensuring the best precision and recall tradeoff. Figure illlstraaess the precision, recall and score curves along with 0. If the chosen threshold is too high, many degraded laser devices will be classified as normal devices, resulting in higher false positive. Whereas the selected threshold is too low, many normal devices will be classified abnormal, leading higher false negative. Therefore, the optimal threshold that maximizes Fl score is chosen. The same selected threshold to distinguish the normal devices from the degraded ones is used for the FL approach.
The performance of the FL approach is compared to the centralized approach using as evaluation metries precision, recall, Fl score and accuracy. The results of the comparison shown in Table prove that the FL framework achieves similar performance as the centralized approach.
Table Comparison of FL and centralized approach
Approach Precision (%)
Recall (%)
Fl score %)
Accuracy (%)
FL Centralized
99.31
93.22 93.54
96.17 16.17
93.42
99.31
96.34
93.71
Attack detection capability evaluation The anomalous weight detection model is compared to defense-based methods namely krum (Blanchard et al. (017)) Trimmed Mean (Yin et al. 2018 and Median. The reconstruction error achieved by the global model for each global round is adopted as evaluation metric. Two adversarial attacks namely additive noise (i.e adding gaussian noise to the model weight) and sign flip (i.e flipping the sign of the model weight) (Li et al.] 2018) are generated by 10% of clients for each round. The results shown in Figure 4 demonstrate that the proposed method significantly outperforms the defense-based approaches for the considered attack scenarios. can seen that the proposed method achieves similar performance as the FedAvg algorithm without attack, which proves the effectiveness of the anomaly detection model in detecting the anomalous weights. The performances of the defense- based methods are worse as they are not effective in defending against attacks for not identically and independently distributed (iid) settings,
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Reconstruction error
FedAvg without attack
1.5
Proposed method
1.00
Trimmed Mean
Reconstruction
1.0
Krum
0.75
Median
0.50
0.5
0.25
0.0
0.00
20
40
60
80
100
0
20
40
60
90
100
Nround
Nround
(a) Additive noise attack
(b) Sign flip attack
Figure 4: Comparison the proposed detection-based method and defense-based approaches in terms of reconstruct error of the global model under two adversarial attacks. Note that the legend in Fig. 4a applies to Fig. 4b as well.
100.0
95.0
F1 score (%)
92.5
90.0
85.0
82.5
80.0
0
10 Percentage of malicious clients (%)
20
Figure 5: Evaluation of the performance
and the fraction of the malicious clients which required by Krum and Trimmed Mean can not be known priori in FL.
We evaluate the performance of the global model in terms Fl score by increasing the percentage of malicious clients to 20%. The results depicted in Fig. shooow that the model can still achieve good performance even the 20% the clients are malicious, which proves that the anomaly detection method is able to recognize the anomalous weights. Note that the training data for FL approach not big (4200 samples) and if 10% or 20% of clients are malicious, and the anomaly detection method accurately detect them and remove the anomalous weights, the global model is trained in such cases with fewer clients, thus fewer data, which might impact the performance of the model.
CONCLUSION
Optical networks require high level of reliability and sustainability. Machine learning techniques are expected to improve maintaining such networks efficiently. We showed that an accurate and clients'
reliable ML model could be developed collaborative learning without the disclosure sensitive datasets even in malicious setting. Our experiments confirm that (i) the presented FL approach achieves a good prediction capability similar to the one yielded by the centralized ap- proach, and (ii) the proposed autoencoder based anomaly detection model is efficient in recognizing the anomalous weights potentially sent by malicious clients, and outperforms the basense-based methods.

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ReportLinker.
Global
predictive
maintenance
industry,
May
2021.
URL
https://wwer rewort.eprr.ioo linker com/p057994117
Global -rredirrtc -a Inciiier-ndduury.... htm ?utm_ sattm_ssurce--NN
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issenixeccaiiyy2///eeeeeaaoooo.. Zheng
A APPENDIX
A. AGGREGATION PROTOCOL
Suppose that server (network provider) builds global ML model for predictive maintenance with N vendors. The pseudo code of the algorithm is given in Algorithm

---

Under review as a conference paper at ICLR 2022
Algorithm Federated averaging algorithm using MPC
Input The vendors are indexed by i; æy is the local training dataset; s, is the linear mask; Pij is the j-th share of the linear mask 8; Output global ML model
for round = 1, 2, ... do
for i 1,2, ...,, N do
f + s |+1 + LocalUpdate((.. fi) q.
++1 + LocalShares(i)
end for
F++1
N (1t+1 +stt1 +9,+1) =im st+1 f
+
(s - - = Σ ft
end for
PocalUpdateit f):
B + (split n; into batches size B)
for each epoch B do for batch
do
f 17 A(f,b)
end for
+ f 8;
end for return fo to the server.
LocalShares(i)
for j = 1,2 N do 4: 5- E=E Pij
end for return qi to the server.
12


Reviews:
[{"review_id": "lBDZ95_iW59", "reviewer": "Reviewer_JagA", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strengths\n- This paper provides an interesting application of using ML.\n\nWeakness\n- Lack of ML technique related novelty: this paper directly leverages the existing ML techniques, eg., FedAvg and AutoEncoder, w/o proposing new techniques. I think this is just a pure ML application paper. \n- Motivation doubt: I wonder whether there will be the malicious venders in the real world. If the vender wants to pollute the global model, then the malicious data/model will affect the vender's own prediction accuracy as well, then why does he/she want to do that. Not sure whether the authors can address the motivation clearer.\n- Why Antoencoder is picked for anomaly detection? Why not the other approaches?", "summary_of_the_paper": "This paper demonstrates that for the problem of doing predictive maintenance in optical networks, accurate and reliable ML models could be developed in collaborative learning without the disclosure of vendors’ data even in malicious setting. The authors did some experiments to show that 1) federated learning can help to achieve similar prediction accuracy as the centralized approach; 2) malicious behaviors may happen in federated learning, but it can be detected through autoencoder.", "summary_of_the_review": "This paper provides an interesting application of using ML.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "EHONubA5AwN", "reviewer": "Reviewer_BxDo", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The focus of this study is not clear, whether it is to solve the challenge of collaborative failure prediction or the problem of encryption aggregation in federated learning (FL). In fact, the related work part only summarized three related studies. I believe this is far from enough to reflect the related research progresses. On the one hand, secure aggregation is a fundamental task of FL. We can see a lot of recent progress (see below, for example). Although, as the authors said, they typically focus on the global model's accuracy, efficiency, and scalability issue. However, it does not mean that they are not suitable for the problem scenarios of collaborative prediction of optical transmitter degradation simply because we do not need these features.\n\n-\tFereidooni, Hossein, et al. \"SAFELearn: secure aggregation for private federated learning.\" IEEE Security and Privacy Workshops (SPW), 2021.\n\n-\tLi, Yong, et al. \"Privacy-Preserving Federated Learning Framework Based on Chained Secure Multiparty Computing.\" IEEE Internet of Things Journal 8.8 (2020): 6178-6186.\n\n-\tKadhe, Swanand, et al. \"Fastsecagg: Scalable secure aggregation for privacy-preserving federated learning.\" arXiv preprint arXiv:2009.11248 (2020).\n\n-\tYang, Chien-Sheng, et al. \"LightSecAgg: Rethinking Secure Aggregation in Federated Learning.\" arXiv preprint arXiv:2109.14236 (2021).\n\n-\tConstance Beguier, et al. \" Efficient Sparse Secure Aggregation for Federated Learning.\" arXiv preprint arXiv:2007.14861 (2021). \n\nOn the other hand, FL-based prediction tasks have also been extensively studied, although they are not necessarily designed for optical network predictive maintenance.\n\nThe motivation of this paper is also unclear. The problem scenario seems unrealistic. To put it bluntly, one can purchase batches of semiconductor laser products from the open market to test themselves, which will produce similar results. Using FL does not change the limitation that vendors can only rely on accelerated aging tests to predict maintenance.\n\nIn terms of innovation, the paper directly uses the FedAvg framework, and the prediction and fault detection methods leveraged are also traditional.\n\nIn the threats analysis part, the paper shows three possible attacks in this specific scenario. They are probably based on the author’s assumptions, or there is no clear evidence to show the purpose of these attacks.\n\nFor example, is it possible for a model inversion attack to obtain real data from other vendors? Why not just buy a product to test directly? \n\nDuring the local model poisoning attack, if an attacker tries to pollute the global model with poisoned data, the prediction results it obtains will also be meaningless, which is contrary to the fundamental motivation as the paper claimed: “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.”\n\nLastly, in the experimental part, the paper splits data into ten pieces to simulate ten clients, which cannot reflect the diversity of products from different manufacturers. \n\nIn addition, there are some obvious language problems, as listed below:\n\n-\tIn Abstract, “it is challenging to develop an accurate and reliable ML based prognostic models” -> accurate and reliable ML based prognostic models, or “an … model”\n\n-\tIn Page 1, Paragraph 1, “global predictive maintenance market is expected” -> the global predictive maintenance market\n\n-\tIn Page 2, Paragraph 5, “a secure aggregation protocol is tolerant to the malicious behavior of participants in a honest-majority model” -> an honest-majority model\n\n-\tIn Page 2, Paragraph 5, “Such framework does not fit for our use case due to multiple reasons.” -> Such a framework\n\n-\tIn Page 2, Paragraph 5, “may give negative impact on the data owner’s business.” -> give a negative impact\n\n-\tIn Page 3, Paragraph 5, “whereby different semiconductor laser manufacturers (i.e vendors) collaborate …” -> (i.e., vendors)\n\n-\tIn Page 5, Paragraph 4, “For example, in Fredrikson et al. (2015), authors demonstrated a model inversion attack” -> the authors\n\n-\tIn Page 7, Paragraph 2, “The architecture of global model is composed of two GRU layers containing each 64 cells.” -> containing 64 cells.", "summary_of_the_paper": "This paper designs a maintenance prediction framework for key optical network components based on federated learning. The designed framework can resist malicious environments and several kinds of attacks. The paper uses simulation data to verify that the proposed method has good predictive ability and can withstand the designed simulation attack.", "summary_of_the_review": "In summary, although the paper tackles a novel problem and designs an FL-based framework for the target, it shows significant defects in its motivation, innovation, and experiments. As a result, I do not recommend accepting the paper.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "7ADIGC3vmpx", "reviewer": "Reviewer_p63q", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "I think this is valuable work and I liked many parts of the paper. However, this work needs to be significantly matured prior to publication. The major issue I had with this paper is that it couldn't decide what it wanted to be. The primary benefit claim was in reducing costs of maintenance, but that was not evaluated. Instead they put extensive time and effort into discussion of security and privacy concerns, without significant ties to machine learning. They also spent extensive time reviewing material that should be assumed background reading for the ICLR audience. \n\nRegarding the primary claim of reduced operational costs, they did not measure this directly. Rather, they evaluated a proxy measures of precision, recall, accuracy, and f1 score, which are incomplete evaluations for operational benefit. The key measure that they left out was the timeliness of detection, and this was not evaluated.\n\nSecurity and privacy are always challenging to evaluate. I give the authors credit for elevating these to first class concerns in this paper. They evaluated privacy and security using robustness as a measure, with different percentages of malicious clients giving motivated \"noisty\" data as input. The \"n-out-of-n\" scheme was a nice application to limited model inversion attacks. They also made effective use of an anomaly detectors to detect data poisoning attacks. While I suspect there are ways to fool an anomaly detector, this still makes such attacks significantly more challenging. \n\nOn a more superficial note, large parts of this paper should be assumed knowledge for an ICLR audience. For example, equations 1-7 do not meaningfully improve any of their results, are not referenced again, and do not help a reader reproduce any of the results. The methods section needs more details on the structure of the training and test datasets, what are the features? how long are the series? How were your experiments setup? Much of this is in section 4.1, but I had to go looking for it. \n\nThe data that was used and baseline efforts were somewhat unsatisfying. Only one non-public dataset was evaluated, and it was unclear how well that dataset reflected many of \"needs\" identified in the use case - splitting a monolotic dataset into 10 parts is closer to a 10-fold cross validation scheme than a federated learning scheme. While splitting the data up this way might be required for federated learning, without clearly identfied splitting criteria (such as client 10 all were all deployed in the same geographic regon, or used hardware from the same vendor), I found it difficult to connect the experiments to the identified needs. \n\n", "summary_of_the_paper": "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. \n\nTheir primary comparison was against a global model.", "summary_of_the_review": "This is a promising, but immature work. It needs to be focused on only one of the identified problems and the evaluation needs to be significantly improved. It seems like there is potential for multiple papers in this work, but it needs to be split up and experiments carried out more carefully. This paper would also benefit from a more direct connection to the identified use case, as well as some justification of why that use case is realistic. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "6Bg9EygTPY", "reviewer": "Reviewer_qcjG", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "•\tThe main challenge in predictive maintenance is often models trained on a specific unit/asset will not generalize and perform well when tested on a different unit of the same type. For example, if the model is trained on a ball bearing under certain operating condition and then tested on a different bearing under a different operating condition, the result will not be good. In the proposed concept of collaborative learning with different datasets from different vendors, it is very unlikely that the different vendors use relatively similar assets. The paper data set is not from different vendors therefore the prediction of the model under such condition will be not acceptable.  This challenge is not addressed in this paper. \n\n•\tThe paper should state exactly whether it is tackling a prognostic (prediction of remaining useful life) or Diagnostic (detection of the fault type) or anomaly detection problem. This is not clear from the paper. In page three authors mention of prognostics, later on an AE is used for anomaly detection. Please elaborate in the paper what type of problem is being solved here.\n\n•\tThe paper uses accelerated testing data, while such data is widely used to build reliability models for different assets, their application on the normal condition experiment is questionable. The model trained on accelerated life tests cannot be tested on normal condition data.\n\n•\tThe description of the dataset should be enhanced in the paper. Not enough information is reported about the types of faults/degradation/experimental setup/differences of the experimental setups used\n\n•\tWhile the concept is based on collaborating learning of different vendors that may use very different assets for the training sets. The dataset that the authors use is not from various/ different assets. There only difference is in operating conditions. Unit to unit variation, similar device manufactured by different vendors with, is a big challenge in predictive maintenance that can not be addressed with the above dataset used in the paper. So my main point is the dataset is not suitable for demonstration of the collaborative learning concept in predictive maintenance. \n\n•\tThe key contribution from the algorithm perspective is not clear in the paper. Please elaborate in the paper if there are any algorithmic contribution in the paper.\n\n•\tThe paper states that a Robust and secure model is developed. However, it does not provide some clear criteria for robustness as well as security. So these aspects of the model should be developed further in details or may be in a separate manuscript/appendix. \n", "summary_of_the_paper": "The paper is on a very interesting topic, predictive maintenance, that has a big impact on various industries. It presents a concept/framework for collaborative learning in predictive maintenance application where a global model is trained based on different vendors training dataset without sharing the data. ", "summary_of_the_review": "While the presented concept of collaborative learning for predictive maintenance can be a key contribution to the domain of PdM, this idea is not well developed by means of related datasets. One suggestion is, the authors use several bearing datasets that are available publicly, i.e. Case Western Reserve University dataset, University of Cincinnati’s dataset and many more available bearing datasets and use them as different vendors and then train the global model. Then the model can be tested on an unseen different dataset. The output should be compared with ground truth if the authors are solving a prognostics problem or be compared to a known fault type if they are solving a diagnostics classification problem. \nWith the above said enhancements needed for the paper, I cannot recommend the paper for the publication. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper presents an optimization technique for optical networks based on federated learning. The motivation for using federated learning stems from the privacy of datasets arising from different operators. The performance of the method is compared to the one based on centralized learning. Despite demonstrating an interesting and promising application of a federated learning, the paper is rather weak in its methodical contribution. Its experimental evaluation, however, is rather artificial with an FL problem generated by splitting the dataset for a centralized problem into parts. No response to the reviewers' comments was provided.
Decision: Reject