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1. WO2020139179 - A WIRELESS DEVICE, A NETWORK NODE AND METHODS THEREIN FOR TRAINING OF A MACHINE LEARNING MODEL

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS

1. A method performed in a wireless device (120) for assisting a network node (1 10) to perform training of a machine learning model, wherein the wireless device (120) and the network node (1 10) operate in a wireless communications system (10) and wherein the method comprises:

- collecting (201) a number of successive data samples for training of the machine learning model comprised in the network node (1 10);

- successively creating (202) compressed data by:

associating each collected data sample to a cluster, which cluster has a cluster centroid, a cluster counter representative of a number of collected data samples determined to be normal and being associated with the cluster, and a number of outlier collected data samples associated with the cluster, wherein the number of outlier collected data samples is a number of collected data samples determined to be anomalous with respect to the cluster,

updating the cluster centroid to correspond to a mean position of all normal data samples that are associated with the cluster, and by

increasing the cluster counter by one for each normal data sample that is associated with the cluster; and

- transmitting (204), to the network node (1 10), the compressed data comprising the cluster centroid, the cluster counter, and the number of outlier collected data samples, which compressed data is to be used in the training of the machine learning model.

2. The method of claim 1 , further comprising:

- storing (203), in a memory (307), the cluster centroid, the cluster counter and the number of outlier collected data samples associated with the cluster as the compressed data.

3. The method of claim 1 or 2, wherein the successively creating (202) of the compressed data comprises:

- associating only a single normal data sample out of the number of collected data samples to each cluster such that the normal data sample is the cluster centroid, the number of normal data samples associated with the cluster is one, and the number of outlier collected data samples associated with the cluster is zero; and when a number of clusters has reached a maximum number, the method further comprises:

- merging one or more of the clusters into a merged cluster by updating the cluster centroid to correspond to a mean position of all associated normal data samples of the one or more clusters, and by determining the cluster counter for the merged cluster to be equal to the number of all normal data samples associated with the one or more clusters.

4. The method of claim 3, wherein the merging of the one or more clusters into the merged cluster comprises:

- merging the one or more clusters into the merged cluster when a determined variance value of the merged cluster is lower than the respective variance value of the one or more clusters.

5. The method of any one of claims 1 - 4, wherein the successively creating (202) of the compressed data further comprises:

- performing anomaly detection between the collected data sample and the associated cluster to determine whether the collected data sample is an anomalous data sample or a normal data sample.

6. The method of claim 5, wherein the performing of the anomaly detection between the collected data sample and the determined associated cluster comprises:

- determining a distance between the cluster centroid of the associated cluster and the collected data sample;

- determining the collected data sample to be an anomalous data sample when the distance is equal to or above a threshold value; and

- determining the collected data sample to be a normal data sample when the distance is below the threshold value.

7. The method of any one of claims 1 -6, comprising:

- determining a maximum number of clusters to be used based on a storage capacity of the memory (307) storing the compressed data.

8. The method of any one of claims 1 -6, comprising:

- determining a maximum number of clusters to be used by increasing a number of clusters until a respective variance value of data samples associated with the respective cluster is below a variance threshold value.

9. The method of any one of claims 1 -8, further comprising:

- determining one or more directions of a multidimensional distribution of the normal data samples associated with the cluster,

- optionally disregarding one or more directions of the multidimensional distribution along which the normal data samples have a variance value for the one or more directions that is below a variance threshold value; and

- transmitting, to the network node (1 10), the variance value for the one or more directions of the normal data samples having a variance value above the variance threshold value.

10. The method of any one of claims 1 -9, wherein the transmitting (204) of the compressed data to the network node (1 10) comprises:

- transmitting the compressed data to the network node (1 10) when a load on a communications link between the wireless device (120) and the network node (1 10) is below a load threshold value; and wherein the method further comprises:

- removing the transmitted compressed data from the memory (307).

1 1. The method of any one of claims 1 -10, further comprising:

- receiving, from the network node (1 10), a request for compressed data to be used in the training of the machine learning model, and wherein the transmitting (204) of the compressed data to the network node (1 10) comprises:

-transmitting the compressed data to the network node (1 10) in response to the received request.

12. A method performed in a network node (1 10) for training of a machine learning model, wherein the network node (1 10) and a wireless device (120) operate in a wireless communications system (10) and wherein the method comprises:

- receiving (401), from the wireless device (120), compressed data corresponding to a cluster centroid, a cluster counter, and a number of outlier collected data samples associated with a cluster, which compressed data is a compressed representation of data samples collected by the wireless device (120); and

- training (402) the machine learning model using the received compressed

data as input to the machine learning model.

13. The method of claim 12, further comprising:

- receiving, from the wireless device (120), a variance value per direction of a multidimensional distribution of the collected data samples associated with the cluster;

- generating a number of random data samples based on the received cluster centroid and the received variance values, wherein the number of random data samples is proportional to the cluster counter; and wherein the training (402) of the machine learning model using the received compressed data as input to the machine learning model further comprises:

- training the machine learning model using the one or more generated random data samples as input to the machine learning model.

14. The method of claim 12 or 13, further comprising:

- updating the machine learning model based on a result of the training.

15. A wireless device (120) for assisting a network node (1 10) to perform training of a machine learning model, wherein the wireless device (120) and the network node (1 10) are configured to operate in a wireless communications system (10) and wherein the wireless device (120) is configured to:

- collect a number of successive data samples for training of the machine learning model comprised in the network node (1 10);

- successively create compressed data by being configured to:

associate each collected data sample to a cluster, which cluster has a cluster centroid, a cluster counter representative of a number of collected data samples determined to be normal and being associated with the cluster, and a number of outlier collected data samples associated with the cluster, wherein the number of outlier collected data samples is a number of collected data samples determined to be anomalous with respect to the cluster,

update the cluster centroid to correspond to a mean position of all normal data samples that are associated with the cluster, and by

increase the cluster counter by one for each normal data sample that is associated with the cluster; and

- transmit, to the network node (1 10), the compressed data comprising the cluster centroid, the cluster counter and the number of outlier collected data samples, which compressed data is to be used in the training of the machine learning model.

16. The wireless device (120) of claim 15, further being configured to:

- store, in a memory (307), the cluster centroid, the cluster counter and the number of outlier collected data samples associated with the cluster as the compressed data.

17. The wireless device (120) of claim 15 or 16, wherein the wireless device (120) is configured to successively create the compressed data by further being configured to:

- associate only a single normal data sample out of the number of collected data samples to each cluster such that the normal data sample is the cluster centroid, the number of normal data samples associated with the cluster is one, and the number of outlier collected data samples associated with the cluster is zero; and

- when a number of clusters has reached a maximum number, merge one or more of the clusters into a merged cluster by updating the cluster centroid to correspond to a mean position of all associated normal data samples of the one or more clusters, and by determining the cluster counter for the merged cluster to be equal to the number of all normal data samples associated with the one or more clusters.

18. The wireless device (120) of claim 17, wherein the wireless device (120) is configured to merge the one or more clusters into the merged cluster by further being configured to:

- merge the one or more clusters into the merged cluster when a determined variance value of the merged cluster is lower than the respective variance value of the one or more clusters.

19. The wireless device (120) of any one of claims 15-18, wherein the wireless device (120) is configured to successively create the compressed data by further being configured to:

- perform anomaly detection between the collected data sample and the associated cluster to determine whether the collected data sample is an anomalous data sample or a normal data sample.

20. The wireless device (120) of claim 15, wherein the wireless device (120) is configured to perform the anomaly detection between the collected data sample and the determined associated cluster by further being configured to:

- determine a distance between the cluster centroid of the associated cluster and the collected data sample;

- determine the collected data sample to be an anomalous data sample when the distance is equal to or above a threshold value; and

- determine the collected data sample to be a normal data sample when the distance is below the threshold value.

21. The wireless device (120) of any one of claims 15-20, being configured to:

- determine a maximum number of clusters to be used based on a storage capacity of the memory (307) storing the compressed data.

22. The wireless device (120) of any one of claims 15-21 , being configured to:

- determine a maximum number of clusters to be used by increasing a number of clusters until a respective variance value of data samples associated with the respective cluster is below a variance threshold value.

23. The wireless device (120) of any one of claims 15-22, being configured to:

- determine one or more directions of a multidimensional distribution of the normal data samples associated with the cluster,

- optionally disregard one or more directions of the multidimensional distribution along which the normal data samples have a variance value for the one or more directions that is below a variance threshold value; and

- transmit, to the network node (1 10), the variance value for the one or more directions of the normal data samples having a variance value above the variance threshold value.

24. The wireless device (120) of any one of claims 15-23, wherein the wireless device (120) is configured to transmit the compressed data to the network node (1 10) by further being configured to:

- transmit the compressed data to the network node (1 10) when a load on a communications link between the wireless device (120) and the network node (1 10) is below a load threshold value; and wherein the wireless device (120) further is configured to:

- remove the transmitted compressed data from the memory (307).

25. The wireless device (120) of any one of claims 15-24, further being configured to:

- receive, from the network node (1 10), a request for compressed data to be used in the training of the machine learning model, and wherein the wireless device (120) is configured to transmit the compressed data to the network node (1 10) by further being configured to:

-transmit the compressed data to the network node (1 10) in response to the received request.

26. A network node (1 10) for training of a machine learning model, wherein the network node (1 10) and a wireless device (120) are configured to operate in a wireless communications system (10) and wherein the network node (1 10) is configured to:

- receive, from the wireless device (120), compressed data corresponding to a cluster centroid, a cluster counter, and a number of outlier collected data samples associated with a cluster, which compressed data is a compressed representation of data samples collected by the wireless device (120); and

- train the machine learning model using the received compressed data as input to the machine learning model.

27. The network node (1 10) of claim 26, further being configured to:

- receive, from the wireless device (120), a variance value per direction of a multidimensional distribution of the collected data samples associated with the cluster;

- generate a number of random data samples based on the received cluster centroid and the received variance values, wherein the number of random data samples is proportional to the cluster counter; and wherein the network node (1 10) is configured to train of the machine learning model using the received compressed data as input to the machine learning model by further being configured to:

- train the machine learning model using the one or more generated random data samples as input to the machine learning model.

28. The network node (1 10) of claim 26 or 27, further being configured to:

- update the machine learning model based on a result of the training.

29. A computer program, comprising instructions which, when executed on at least one processor, causes the at least one processor to carry out the method according to any one of claims 1 -14.

30. A carrier comprising the computer program of claim 29, wherein the carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium.