Does your implementation code of FedProx correspond to the algorithm block 2 in the original paper of FedProx? More specifically, the formula for updating lines 53-54 of code file "fedoptimizer.py" seems a little strange, right? In particular, what does lambda mean in FedProx algorithm?
The update formula I understand should be :
p.data=p.data - group['lr'] ( p.grad. data + group ['mu'] (p.data - pstar.data.clone())
Hi.
Does your implementation code of FedProx correspond to the algorithm block 2 in the original paper of FedProx? More specifically, the formula for updating lines 53-54 of code file "fedoptimizer.py" seems a little strange, right? In particular, what does lambda mean in FedProx algorithm?
The update formula I understand should be : p.data=p.data - group['lr'] ( p.grad. data + group ['mu'] (p.data - pstar.data.clone())
Looking forward to your reply.