Federated learning, where a global model is trained by iterative parameter averaging of locally-computed updates, is a promising approach for distributed training of deep networks; it provides high communication-efficiency and privacy-preservability, which allows to fit well into decentralized data environments, e.g., mobile-cloud ecosystems. However, despite the advantages, the federated learning-based methods still have a challenge in dealing with non-IID training data of local devices (i.e., learners). In this regard, we study the effects of a variety of hyperparametric conditions under the non-IID environments, to answer important concerns in practical implementations: (i) We first investigate parameter divergence of local updates to explain performance degradation from non-IID data. The origin of the parameter divergence is also found both empirically and theoretically. (ii) We then revisit the effects of optimizers, network depth/width, and regularization techniques; our observations show that the well-known advantages of the hyperparameter optimization strategies could rather yield diminishing returns with non-IID data. (iii) We finally provide the reasons of the failure cases in a categorized way, mainly based on metrics of the parameter divergence. Over the recent years, federated learning (McMahan et al., 2017) has been a huge success to reduce the communication overhead in distributed training of deep networks. Guaranteeing competitive performance, the federated learning permits each learner to compute their local updates of each round for relatively many iterations (e.g., 1 epoch, 10 epochs, etc.), which provides much higher communication-efficiency compared to the conventional data parallelism approaches (for intra-datacenter environments, e.g., Dean et al. (2012) ; Chen et al. (2016) ) that generally require very frequent gradient aggregation. Furthermore, the federated learning can also significantly reduce data privacy and security risks by enabling to conceal on-device data of each learner from the server or other learners; thus the approach can be applied well to environments with highly private data (e.g., personal medical data), it is now emerging as a promising methodology for privacypreserving distributed learning along with differential privacy-based methods (Hard et al., 2018; Yang et al., 2018; Bonawitz et al., 2019; Chen et al., 2019) . On this wise, the federated learning takes a simple approach that performs iterative parameter averaging of local updates computed from each learners' own dataset, which suggests an efficient way to learn a shared model without centralizing training data from multiple sources; but hereby, since the local data of each device is created based on their usage pattern, the heterogeneity of training data distributions across the learners might be naturally assumed in real-world cases. Hence, each local dataset would not follow the population distribution, and handling the decentralized non-IID data still remains a statistical challenge in the field of federated learning (Smith et al., 2017) . For instance, Zhao et al. (2018) observed severe performance degradation in multi-class classification accuracy under highly skewed non-IID data; it was reported that more diminishing returns could be yielded as the probabilistic distance of learners' local data from the population distribution increases. We now explain the internal reasons of the observations in the previous subsection. Through the experimental results, we were able to classify the causes of the failures under non-IID data into three categories; the following discussions are described based on this. 8 Note that our discussion in this subsection is mostly made from the results under Nesterov momentum SGD and on CIFAR-10; the complete results including other optimizers (e.g., pure SGD, Polyak momentum SGD, and Adam) and datasets (e.g., SVHN) are given in Appendix C. Inordinate magnitude of parameter divergence. As mentioned before, bigger parameter divergence is the root cause of diminishing returns under federated learning methods with non-IID data. By extension, here we observe that even under the same non-IID data setting, some of the considered hyperparametric methods yield greater parameter divergence than when they are not applied. For example, from the left plot of Figure 3 , we see that under the Non-IID(2) setting, the parameter divergence values (in the last fully-connected layer) become greater as the network depth increases (note that NetA-Baseline, NetA-Deeper, and NetA-Deepest have 3, 6, and 9 convolutional layers, respectively; see also Appendix A.1 for their detailed architecture). The corresponding final test accuracy was found to be 74.11%, 73.67%, and 68.98%, respectively, in order of the degree of shallowness; this fits well into the parameter divergence results. Since the NetA-Deeper and NetA-Deepest have twice and three times as many model parameters as NetA-Baseline, it can be expected enough that the deeper models yield bigger parameter divergence in the whole model; but our results also show its qualitative increase in a layer level. In relation, we also provide the results using the modern network architecture (e.g., ResNet (He et al., 2016) ) in Table 8 of the appendix. From the middle plot of the figure, we can also observe bigger parameter divergence in a high level of weight decay under the Non-IID(2) setting. Under the non-IID data setting, the test accuracy of about 72 ∼ 74% was achieved in the low levels (≤ 0.0001), but weight decay factor of 0.0005 yielded only that of 54.11%. Hence, this suggests that with non-IID data we should apply much smaller weight decay to federated learning-based methods. Here we note that if a single iteration is considered for each learner's local update per round, the corresponding parameter divergence will be of course the same without regard to degree of weight decay. However, in our experiments, the great number of local iterations per round (i.e., 100) made a big difference of the divergence values under the non-IID data setting; this eventually yielded the accuracy gap. We additionally observe for the non-IID cases that even with weight decay factor of 0.0005, the parameter divergence values are similar to those with the smaller factors at very early rounds in which the norms of the weights are relatively very small. In addition, it is observed from the right plot of the figure that Dropout (Hinton et al., 2012; Srivatava et al., 2014 ) also yields bigger parameter divergence under the non-IID data setting. The corresponding test accuracy was seen to be a diminishing return with Nesterov momentum SGD (i.e., using Dropout we can achieve +2.85% under IID, but only +1.69% is obtained under non-IID(2), compared to when it is not applied; see Table 2 ); however, it was observed that the generalization effect of the Dropout is still valid in test accuracy for the pure SGD and the Adam (refer to also Table 13 in the appendix). Steep fall phenomenon. As we see previously, inordinate magnitude of parameter divergence is one of the notable characteristics for failure cases under federated learning with non-IID data. However, under the non-IID data setting, some of the failure cases have been observed where the test accuracy is still low but the parameter divergence values of the last fully-connected layer decrease (rapidly) over rounds; as the round goes, even the values were sometimes seen to be lower than those of the comparison targets. We refer to this phenomenon as steep fall phenomenon. It is inferred that these (unexpected abnormal) sudden drops of parameter divergence values indicate going into poor local minima (or saddles); this can be supported by the behaviors that test accuracy increases plausibly at very early rounds, but the growth rate quickly stagnates and eventually becomes much lower than the comparison targets. The left plot of Figure 4 shows the effect of the Adam optimizer with respect to its implementations. Through the experiments, we identified that under non-IID data environments, the performance of Adam is very sensitive to the range of model variables to be averaged, unlike the non-adaptive optimizers (e.g., momentum SGD); its moment variables should be also considered in the parameter averaging together with weights and biases (see also Table 3 ). The poor performance of the Adam-WB under the Non-IID(2) setting would be from twice as many momentum variables as the momentum SGD, which indicates the increased number of them affected by the non-IIDness; thus, originally we had thought that extreme parameter divergence could appear if the momentum variables are not averaged together with weights and biases. However, it was seen that the parameter divergence values under the Adam-WB was seen to be similar or even smaller than under Adam-A (see also Figure 11 in the appendix). Instead, from the left panel we can observe that the parameter divergence of Adam-WB in the last fully-connected layer is bigger than that of Adam-A at the very early rounds (as we expected), but soon it is abnormally sharply reduced over rounds; this is considered the steep fall phenomenon. The middle and the right plots of the figure also show the steep fall phenomenon in the last fullyconnected layer, with respect to network width and whether to use Batch Normalization, respectively. In the case of the NetC models, NetC-Baseline, NetC-Wider, and NetC-Widest use the global average pooling, the max pooling with stride 4, and the max pooling with stride 2, respectively, after the last convolutional layer; the number of neurons in the output layer becomes 2560, 10240, and 40960, respectively (see also Appendix A.1 for their detailed architecture). Under the Non-IID(2) setting, the corresponding test accuracy was found to be 64.06%, 72.61%, and 73.64%, respectively, in order of the degree of wideness. In addition, we can see that under Non-IID(2), Batch Normalization 9 yields not only big parameter divergence (especially before the first learning rate drop) but also the steep fall phenomenon; the corresponding test accuracy was seen to be very low (see Table 3 ). The failure of the Batch Normalization stems from that the dependence of batchnormalized hidden activations makes each learner's update too overfitted to the distribution of their local training data. Batch Renormalization, by relaxing the dependence, yields a better outcome; however, it still fails to exceed the performance of the baseline due to the significant parameter divergence. To explain the impact of the steep fall phenomenon in test accuracy, we provide Figure 5 , which indicates that the loss landscapes for the failure cases (e.g., Adam-WB and with Batch Normalization) commonly show sharper minima that leads to poorer generalization (Hochreiter & Schmidhuber, 9 For its implementations into the considered federated learning algorithm, we let the server get the proper moving variance by 1997; Keskar et al., 2017) , and the minimal value in the bowl is relatively greater. 10 Here it is also observed that going into sharp minima starts even in early rounds such as 25th. Excessively high training loss of local updates. The final cause that we consider for the failure cases is excessively high training loss of local updates. For instance, from the left plot of Figure 6 , we see that under the Non-IID(2) setting, NetB-Baseline gives much higher training loss than the other models. Here we note that for the NetB-Baseline model, the global average pooling is applied after the last convolutional layer, and the number of neurons in the first fully-connected layer thus becomes 256 · 256; on the other hand, NetB-Wider and NetB-Widest use the max pooling with stride 4 and 2, which make the number of neurons in that layer become 1024 · 256 and 4096 · 256, respectively (see also Appendix A.1 for their details). The experimental results were shown that NetB-Baseline has notably lower test accuracy (see Table 4 ). We additionally remark that for NetBBaseline, very high losses are observed under the IID setting, and their values even are greater than in the non-IID case; however, note that one have to be aware that local updates are extremely easy to be overfitted to each training dataset under non-IID data environments, thus the converged training losses being high is more critical than the IID cases. The middle and the right plot of the figure show the excessive training loss under the non-IID setting when applying the weight decay factor of 0.0005 and the data augmentation, respectively. In the cases of the high level of weight decay, the severe performance degradation appears compared to when the levels are low (i.e., ≤ 0.0001) as already discussed. In addition, we observed that with Nesterov momentum SGD, the data augmentation yields a diminishing return in test accuracy (i.e., with the data augmentation we can achieve +3.36% under IID, but −0.16% is obtained under non-IID(2), compared to when it is not applied); with Adam the degree of the diminishment becomes higher (refer to Table 12 in the appendix). In the data augmentation cases, judging from that the 10 Based on Li et al. (2018) , the visualization of loss surface was conducted by L(α, β) = (θ * + αδ + βγ), where θ * is a center point of the model parameters, and δ and γ is the orthogonal direction vectors. parameter divergence values are not so different between with and without it, we can identify that the performance degradation stems from the high training loss (see Figures 30 and 31 in the appendix). Here we additionally note that unlike on the CIFAR-10, in the experiments on SVHN it was seen that the generalization effect of the data augmentation is still valid in test accuracy (see Table 12 ). In this paper, we explored the effects of various hyperparameter optimization strategies for optimizers, network depth/width, and regularization on federated learning of deep networks. Our primary concern in this study was lied on non-IID data, in which we found that under non-IID data settings many of the probed factors show somewhat different behaviors compared to under the IID setting and vanilla training. To explain this, a concept of the parameter divergence was utilized, and its origin was identified both empirically and theoretically. We also provided the internal reasons of our observations with a number of the experimental cases. In the meantime, the federated learning has been vigorously studied for decentralized data environments due to its inherent strength, i.e., high communication-efficiency and privacy-preservability. However, so far most of the existing works mainly dealt with only IID data, and the research to address non-IID data has just entered the beginning stage very recently despite its high real-world possibility. Our study, as one of the openings, handles the essential factors in the federated training under the non-IID data environments, and we expect that it will provide refreshing perspectives for upcoming works. A EXPERIMENTAL DETAILS
We investigate the internal reasons of our observations, the diminishing effects of the well-known hyperparameter optimization methods on federated learning from decentralized non-IID data.
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