User equipment-coordination set federated for deep neural networks
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for User Equipment-Coordination Set (UECS) Federated for Deep Neural Networks. A coordinating user equipment (UE) of the UECS communicates, to a second UE in the UECS and using one or more side links, one or more update conditions that indicate when to generate updated machine learning (ML) configuration information for one or more deep neural networks (DNNs) that are configured to perform some or all of a transmitter or a receiver processing functionality to process communications at the second UE. The coordinating UE receives, from the second UE over the one or more side links, one or more reports, each report including the updated ML configuration information determined by the second UE using a training procedure and input data local to the second UE.
1 . A method performed by a coordinating user equipment, UE, in a user equipment-coordination set, UECS, of a wireless communication system, for determining a common UECS machine-learning, ML, configuration using federated learning, the UECS including at least two UEs configured to at least one of: jointly transmit uplink data generated by a target UE of the UECS, or jointly receive downlink data intended for the target UE, the method comprising:
communicating, to a second UE in the UECS and using one or more side links, one or more update conditions that indicate when to generate updated ML configuration information for one or more deep neural networks, DNNs, that are configured to perform some or all of a transmitter or a receiver processing functionality to process communications at the second UE;
receiving, from the second UE over the one or more side links, one or more reports, each report including the updated ML configuration information determined by the second UE using a training procedure and input data local to the second UE;
determining the common UECS ML configuration by applying federated learning techniques to the updated ML configuration information received in the one or more reports; and
directing at least one UE of a subset of UEs to update at least one DNN using the common UECS ML configuration.
2 . The method as recited in claim 1 , wherein the one or more update conditions comprise at least:
a schedule; or
a trigger event.
3 . The method as recited in claim 2 , wherein the one or more update conditions comprises the trigger event, and
wherein the trigger event comprises:
one or more ML parameters of the at least one DNN changing by more than a first threshold value;
an ML architecture of the at least one DNN changing;
a first signal or link quality parameter changing by more than a second threshold value; or
a UE-location changing by at least a third threshold value.
4 . The method as recited in claim 1 , wherein receiving the one or more reports further comprises:
receiving the updated ML configuration information for at least one of:
a first DNN that processes incoming communications from a base station;
a second DNN that processes outgoing communications to the base station;
a third DNN that processes incoming side-link communications from the coordinating UE;
a fourth DNN that processes outgoing side-link communications to the coordinating UE;
a fifth DNN that processes incoming side-link communications from another UE in the UECS;
a sixth DNN that processes outgoing side-link communications to the other UE in the UECS; or
a seventh DNN that processes other side-link communications in the UECS.
5 . The method as recited in claim 1 , further comprising: communicating the common UECS ML configuration to a base station.
6 . The method as recited in claim 1 , wherein the common UECS ML configuration is a first common UECS ML configuration, the method further comprising:
receiving, over a side link and from a second coordinating UE of a second UECS, a second common UECS ML configuration; and
determining an updated common UECS ML configuration by combining the second common UECS ML configuration with the first common UECS ML configuration, and
wherein directing each UE to update the at least one DNN using the common UECS ML configuration further comprises:
directing at least one UE of the subset of UEs to use the updated common UECS ML configuration.
7 . The method as recited in claim 1 , wherein determining the common UECS ML configuration further comprises:
determining at least one of:
an ML architecture; or
one or more ML parameters.
8 . The method as recited in claim 1 :
wherein receiving the one or more reports including the updated ML configuration information further comprises:
receiving, from the second UE, current UE characteristics; and
further comprising:
analyzing the current UE characteristics;
determining to reset UECS federated learning for the subset of UEs; and
determining a new UECS machine learning configuration.
9 . A method performed by a user equipment configured as a coordinating user equipment, UE, in a user equipment-coordination set, UECS, of a wireless communication system, for determining at least one common UECS machine-learning, ML, configuration using federated learning, the UECS including at least two UEs configured to at least one of: jointly transmit uplink data generated by a target UE of the UECS, or jointly receive downlink data intended for the target UE, the method comprising:
identifying a subset of UEs, in the UECS to perform peer-to-peer federated learning for one or more DNNs using a training procedure and data local to each UE in the subset of UEs, wherein the one or more DNNs are each configured to perform some or all of a transmitter or a receive processing functionality to process communications at each UE;
directing each UE in the subset of UEs to perform the peer-to-peer federated learning using a training procedure and data local to each UE in the subset of UEs; and
communicating, to each UE in the subset of UEs, one or more update conditions that indicate when to perform the peer-to-peer federated learning.
10 . The method as recited in claim 9 , further comprising:
assigning air interface resources to each UE in the subset of UEs for use in performing the peer-to-peer federated learning; and
communicating the assigned air interface resources to each UE in the subset of UEs.
11 . The method as recited in claim 9 , wherein identifying the subset of UEs further comprises:
selecting at least two UEs, from the UECS, with one or more:
common UE capabilities;
commensurate signal or link quality parameters; or
commensurate UE-locations.
12 . The method as recited in claim 9 , wherein identifying the subset of UEs further comprises:
receiving, from a base station, a selection of UEs in the UECS to include in the subset of UEs.
13 . The method as recited in claim 9 , further comprising:
receiving, from at least one UE in the subset of UEs, an indication of a common UECS ML configuration determined by the subset of UEs using the peer-to-peer federated learning.
14 . The method as recited in claim 9 , further comprising:
directing each UE in the subset of UEs to communicate an updated ML configuration information to a particular UE in the subset of UEs.
15 . A user equipment, UE, comprising:
a processor; and
computer-readable storage media comprising instructions, responsive to execution by the processor, for directing the user equipment to perform a method for determining a common UECS machine-learning, ML, configuration using federated learning, the UECS including at least two UEs configured to at least one of: jointly transmit uplink data generated by a target UE of the UECS, or jointly receive downlink data intended for the target UE, the method comprising:
communicating, from a coordinating UE to a second UE in the UECS and using one or more side links, one or more update conditions that indicate when to generate updated ML configuration information for one or more deep neural networks, DNNs, that are configured to perform some or all of a transmitter or a receiver processing functionality to process communications at the second UE;
receiving, from the second UE over the one or more side links, one or more reports, each report including the updated ML configuration information determined by the second UE using a training procedure and input data local to the second UE;
determining the common UECS ML configuration by applying federated learning techniques to the updated ML configuration information received in the one or more reports; and
directing at least one UE of a subset of UEs to update at least one DNN using the common UECS ML configuration.
16 . The user equipment as recited in claim 15 , wherein the one or more update conditions comprise at least:
a schedule; or
a trigger event.
17 . The user equipment as recited in claim 16 , wherein the one or more update conditions comprises the trigger event, and
wherein the trigger event comprises:
one or more ML parameters of the at least one DNN changing by more than a first threshold value;
an ML architecture of the at least one DNN changing;
a first signal or link quality parameter changing by more than a second threshold value; or
a UE-location changing by at least a third threshold value.
18 . The user equipment as recited in claim 15 , wherein receiving the one or more reports further comprises:
receiving the updated ML configuration information for at least one of:
a first DNN that processes incoming communications from a base station;
a second DNN that processes outgoing communications to the base station;
a third DNN that processes incoming side-link communications from the coordinating UE;
a fourth DNN that processes outgoing side-link communications to the coordinating UE;
a fifth DNN that processes incoming side-link communications from another UE in the UECS;
a sixth DNN that processes outgoing side-link communications to the other UE in the UECS; or
a seventh DNN that processes other side-link communications in the UECS.
19 . The user equipment as recited in claim 15 , further comprising:
communicating the common UECS ML configuration to a base station.
20 . The user equipment as recited in claim 15 , wherein the common UECS ML configuration is a first common UECS ML configuration, the method further comprising:
receiving, over a side link and from a second coordinating UE of a second UECS, a second common UECS ML configuration; and
determining an updated common UECS ML configuration by combining the second common UECS ML configuration with the first common UECS ML configuration, and
wherein directing each UE to update the at least one DNN using the common UECS ML configuration further comprises:
directing at least one UE of the subset of UEs to use the updated common UECS ML configuration.