A Homomorphic Signcryption-Based Privacy Preserving Federated Learning Framework for IoTs

Algorithm 1

Model training phase of client .

	Input: Training round , model parameters , dataset ,quantization bit width , blinding factors , , random seed , Paillier key pairs , secret value
	Output: Signcryption of the masked gradients
	functionMODELTRAINING
	Compute gradients based on and
	Send layer-wise gradients Max-Min values and sizes to the aggregation server
	Clip with corresponding threshold (Advance Scaling) and quantize them into bits
	Batch the quantized gradients layer by layer into
	Blind with to compute
	Sign each blinded gradient with , use the PRG to generate the synchronizing random number , compute the signcryption .
	Send to the aggregation server
	end function