Async - FL Model

Question

How to perform asynchronous model training using TFF framework?

I review the iterative training process loop, however I am not sure how to know which clients models are received.

Answer 1

Its quite possible to simulate something akin to "asynchronous FL" in TFF. One way to think about this could be to conceptually decouple the simulation time from wall clock time.

Sampling different numbers of clients each round (rather than the uniform K clients that is commonly done), perhaps with some distribution that weights clients based on how long they are expected to train, could simulate asynchronous FL. Its possible to only process a portion of the selected clients first, the researcher has the freedom to slice up the data/computation as they desired.

Python-esque pseudo code demonstrates the two techniques, different client sampling, and delayed gradient application:

state = fed_avg_iter_proc.initialize()
for round_num in range(NUM_ROUNDS):
  # Here we conceptualize a "round" as a block of time, rather than a synchronous
  # round. We have a function that determines which clients will "finish" within 
  # our configured block of time. This might even return only a single client.
  participants = get_next_clients(time_window=timedelta(minutes=30))
  num_participants = len(participants)

  # Here we only process the first half, and then updated the global model.
  state2, metrics = fed_avg_iter_proc.next(state, participants[:num_participants/2])

  # Now process the second half of the selected clients. 
  # Note: this is now apply the 'pseudo-gradient' that was computed on clients
  # (the difference between the original `state` and their local training result),
  # to the model that has already taken one step (`state2`). This possibly has
  # undesirable effects on the optimisation process, or may be improved with
  # techniques that handle "stale" gradients.
  state3, metrics = fed_avg_iter_proc.next(state2, participants[num_participants/2:])

  # Finally update the state for the next for-loop of the simulation.
  state = state3