Federated learning (FL) facilitates a privacy-preserving neural network training paradigm through collaboration between edge clients and a central server. One significant challenge is that the distributed data is not independently and identically distributed (non-IID), typically including both intra-domain and inter-domain heterogeneity. However, recent research is limited to simply using averaged signals as a form of regularization and only focusing on one aspect of these non-IID challenges. Given these limitations, this paper clarifies these two non-IID challenges and attempts to introduce cluster representation to address them from both local and global perspectives. Specifically, we propose a dual-clustered feature contrast-based FL framework with dual focuses. First, we employ clustering on the local representations of each client, aiming to capture intra-class information based on these local clusters at a high level of granularity. Then, we facilitate cross-client knowledge sharing by pulling the local representation closer to clusters shared by clients with similar semantics while pushing them away from clusters with dissimilar semantics. Second, since the sizes of local clusters belonging to the same class may differ for each client, we further utilize clustering on the global side and conduct averaging to create a consistent global signal for guiding each local training in a contrastive manner. Experimental results on multiple datasets demonstrate that our proposal achieves comparable or superior performance gain under intra-domain and inter-domain heterogeneity.

State-of-the-art deep learning algorithms mostly rely on gradient backpropagation to train a deep artificial neural network, which is generally regarded to be biologically implausible. For a network of stochastic units trained on a reinforcement learning task or a supervised learning task, one biologically plausible way of learning is to train each unit by REINFORCE. In this case, only a global reward signal has to be broadcast to all units, and the learning rule given is local, which can be interpreted as reward-modulated spike-timing-dependent plasticity (R-STDP) that is observed biologically. Although this learning rule follows the gradient of return in expectation, it suffers from high variance and cannot be used to train a deep network in practice. In this paper, we propose an algorithm called MAP propagation that can reduce this variance significantly while retaining the local property of learning rule. Different from prior works on local learning rules (e.g. Contrastive Divergence) which mostly applies to undirected models in unsupervised learning tasks, our proposed algorithm applies to directed models in reinforcement learning tasks. We show that the newly proposed algorithm can solve common reinforcement learning tasks at a speed similar to that of backpropagation when applied to an actor-critic network.

Deep convolutional network has been the state-of-the-art approach for a wide variety of tasks over the last few years. Its successes have, in many cases, turned it into the default model in quite a few domains. In this work we will demonstrate that convolutional networks have limitations that may, in some cases, hinder it from learning properties of the data, which are easily recognizable by traditional, less demanding, models. To this end, we present a series of competitive analysis studies on image recognition and text analysis tasks, for which convolutional networks are known to provide state-of-the-art results. In our studies, we inject a truth-reveling signal, indiscernible for the network, thus hitting time and again the network's blind spots. The signal does not impair the network's existing performances, but it does provide an opportunity for a significant performance boost by models that can capture it. The various forms of the carefully designed signals shed a light on the strengths and weaknesses of convolutional network, which may provide insights for both theoreticians that study the power of deep architectures, and for practitioners that consider to apply convolutional networks to the task at hand.