Personalized Federated Learning with Semisupervised Distillation

Algorithm 3

Client update.

 Input: communication round , local labeled data , local unlabeled data and public data for each client k, number of local epochs , batch size of labeled data, batch size of public data, batch size of unlabeled data, confidence threshold , learning rate , loss weight and

 Output: model’s predicted class distribution Initialize the local model

  split into batches of size

  split into batches of size

 ifthen

  for each local epoch e = 0, 1, 2, …, E − 1 do

   for each batch , and do

    Compute by equation (1)

   end

  end

 else

  for each local epoch e = 0, 1, 2, …, E − 1 do

   for each batch , and do

    Compute by equations (6)–(9)

   end

  end

 end

 return to server