Sampling user equipment for federated learning model collection

An apparatus for use at network side of a cellular communication system, the apparatus comprising:

• at least one processor;
• and
• at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to perform:
• detecting first user equipment out of a plurality of user equipment of the cellular communication system,
• wherein the user equipment respectively corresponds to a distributed node of a federated machine-learning concept and respectively generate a partial machine-learning model,
• wherein partial machine-learning models generated by the plurality of user equipment are to be used to update a global machine-learning model at the network side of the cellular communication system,
• wherein the first user equipment are user equipment comprising ready partial machine-learning models;
• the at least one memory and computer program code being further configured, with the at least one processor, to cause the apparatus to perform:
• selecting, out of the first user equipment, second user equipment at least based on a time information associated with the first user equipment;
• acquiring the ready partial machine-learning models respectively generated by the second user equipment;
• updating the global machine-learning model using the ready partial machine-learning models acquired;
• determining convergence of the global machine-learning model updated by the ready partial machine-learning models acquired;
• and
• in case convergence of the global machine-learning model is not determined, repeating a process comprising the detecting, selecting, acquiring, updating and determining,
• wherein the selecting comprises:
• prioritizing the first user equipment based on their time information;
• prioritizing the first user equipment prioritized based on their time information also based on channel conditions associated with the first user equipment;
• selecting the second user equipment out of the first user equipment which have been prioritized based on their time information and channel conditions, based on a quota of uplink resources available for acquiring ready partial machine-learning models;
• and
• selecting a user equipment of the plurality of user equipment as second user equipment also based on a number of times of repeating the process in which the user equipment has been detected as first user equipment and has been prioritized based on the time information and has not been selected as second user equipment.