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Awesome of Federated Learning

Developments

Grpc defined

rpc UpdateIdx_uint32(IdxRequest_uint32) returns (IdxResponse_uint32){}
rpc UpdateGrad_int32(GradRequest_int32) returns (GradResponse_int32){}
rpc UpdateGrad_float(GradRequest_float) returns (GradResponse_float){}
rpc DataTrans_int32(DataRequest_int32) returns (DataResponse_int32){}

Server

  1. FlGrpcServer class:
# collect data
def process(self, dict_data, handler)
# start server
def start(self)
  1. Define your own Server, inherit FlGrpcServer, implement the functions define in Grpc
from Common.Server.fl_grpc_server import FlGrpcServer

class yourServer(FLGrpcServer):
    # implement your func.
    def yourfunc(self, request, context):
        pass
  1. implememt your hander
# define interface
class YourHandler:
    def computation(self, data_in):
        return data_out

Worker

  1. WorkerBase: one pass train
  2. Define your Woker,inherit WorkerBase
from Common.Node.workerbase import WorkerBase

class YourWorker(WorkerBase):
    def update():
        gradients = super().get_gradients()
        
        # define upload / download fucntion
        
        super().set_gradients()    
    

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Contributors

karry520 avatar ye-d avatar

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