问Async - FL模型

EN

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