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1. WO2021144803 - CONTEXT-LEVEL FEDERATED LEARNING

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

[ EN ]

CLAIMS:

1. A method performed by a local client computing device, the method comprising: training a local model using data from the local client computing device, resulting in a local model update;

sending the local model update to a central server computing device;

receiving from the central server computing device a first updated global model; determining that the first updated global model does not meet a local criteria;

in response to determining that the first updated global model does not meet a local criteria, sending to the central server computing device context information; and

receiving from the central server computing device a second updated global model.

2. The method of claim 1, wherein determining that the first updated global model does not meet a local criteria comprises computing a score based on the first updated global model, wherein the score exceeds a threshold.

3. The method of any one of claims 1-2, wherein the computed score comprises an error information in a prediction.

4. The method of any one of claims 1-3, wherein the context information includes the computed score.

5. The method of any one of claims 1-4, wherein the context information is encoded prior to sending by using an auto-encoder.

6. The method of any one of claims 1-5, wherein the local model predicts items for stocking a store corresponding to the local client computing device to optimize supply chain management, and the context information comprises one or more of: an area of a market the store covers, a weight of the predicted items, a number of pre-orders for the predicted items, seasonal events related to the predicted items, and a price of the predicted items.

7. The method of any one of claims 1-5, wherein the local model predicts a travel itinerary for a user corresponding to the local client computing device to optimize travel plan selection.

8. A method performed by a central server computing device, the method comprising: receiving from a local client computing device a local model update;

training a global model using the local model update, resulting in a first updated global model;

sending to the local client computing device the first updated global model; receiving from the local client computing device a context information;

training the global model using the local model update and the context information, resulting in a second updated global model; and

sending to the local client computing device the second updated global model.

9. The method of claim 8, wherein training the global model using the local model update and the context information, resulting in a second updated global model comprises using a modified objective function to incorporate the context information.

10. The method of any one of claims 8-9, wherein the context information includes an error information in a prediction from the local client computing device.

11. The method of any one of claims 8- 10, wherein the received context information is encoded by using an auto-encoder.

12. The method of any one of claims 8-11, wherein the local model predicts items for stocking a store corresponding to the local client computing device to optimize supply chain management, and the context information comprises one or more of: an area of a market the store covers, a weight of the predicted items, a number of pre-orders for the predicted items, seasonal events related to the predicted items, and a price of the predicted items.

13. The method of any one of claims 8-11, wherein the local model predicts a travel itinerary for a user corresponding to the local client computing device to optimize travel plan selection.

14. A local client computing device comprising:

a memory; and

a processor, wherein said processor is configured to:

train a local model using data from the local client computing device, resulting in a local model update;

send the local model update to a central server computing device;

receive from the central server computing device a first updated global model; determine that the first updated global model does not meet a local criteria;

in response to determining that the first updated global model does not meet a local criteria, send to the central server computing device context information; and

receiving from the central server computing device a second updated global model.

15. The local client computing device of claim 14, wherein determining that the first updated global model does not meet a local criteria comprises computing a score based on the first updated global model, wherein the score exceeds a threshold.

16. The local client computing device of any one of claims 14-15, wherein the computed score comprises an error in a prediction.

17. The local client computing device of any one of claims 14-16, wherein the context information includes the computed score.

18. The local client computing device of any one of claims 14-17, wherein the context information is encoded prior to sending by using an auto-encoder.

19. The local client computing device of any one of claims 14-18, wherein the local

model predicts items for stocking a store corresponding to the local client computing device to optimize supply chain management, and the context information comprises one or more of: an area of a market the store covers, a weight of the predicted items, a number of pre-orders for the predicted items, seasonal events related to the predicted items, and a price of the predicted items.

20. The local client computing device of any one of claims 14-18, wherein the local model predicts a travel itinerary for a user corresponding to the local client computing device to optimize travel plan selection.

21. A central server computing device comprising:

a memory; and

a processor, wherein said processor is configured to:

receive from a local client computing device a local model update;

train a global model using the local model update, resulting in a first updated global model;

send to the local client computing device the first updated global model;

receive from the local client computing device context information;

train the global model using the local model update and the context information, resulting in a second updated global model; and

send to the local client computing device the second updated global model.

22. The central server computing device of claim 21, wherein training the global model using the local model update and the context information, resulting in a second updated global model comprises using a modified objective function to incorporate the context information.

23. The central server computing device of any one of claims 21-22, wherein the context information includes error information from the local client computing device.

24. The central server computing device of any one of claims 21-23, wherein the received context information is encoded by using an auto-encoder.

25. The central server computing device of any one of claims 21-24, wherein the local model predicts items for stocking a store corresponding to the local client computing device to optimize supply chain management, and the context information comprises one or more of: an area of a market the store covers, a weight of the predicted items, a number of pre-orders for the predicted items, seasonal events related to the predicted items, and a price of the predicted items.

26. The central server computing device of any one of claims 21-24, wherein the local model predicts a travel itinerary for a user corresponding to the local client computing device to optimize travel plan selection.

27. A computer program comprising instructions which when executed by processing circuitry causes the processing circuitry to perform the method of any one of claims 1-13.

28. A carrier containing the computer program of claim 28, wherein the carrier is one of an electronic signal, an optical signal, a radio signal, and a computer readable storage medium.