Table 3.

Ablation study with different variations of VGG16 architecture on IID and non-IID dataset in a centralized learning environment with vanilla and weighted averaging.

Variants	Trainable parameters	Data distribution	Accuracy
			Vanilla avg.	Weighted avg.
VGG16 (5 blocks)	23,112,520	IID	0.94	0.95
		Non-IID	0.93	0.94
VGG16 (4 blocks)	41,198,920	IID	0.93	0.94
		Non-IID	0.92	0.93
VGG16 (3 blocks)	68,853,576	IID	0.92	0.94
		Non-IID	0.91	0.93