Federated learning (FL) is at the core of intelligent Internet architecture. It allows clients to jointly train a model without direct data sharing. In such a process, clients and the central server share information through communication channels formed by parameters. However, the non-iid training data in clients significantly impacts global model convergence and brings difficulties for the evaluation of local contributions. Most of existing studies try to expand the communication channel by improving consistency with variance reduction or regularization, but such methods neglect an important factor, i.e., channel utilization, hence their capability for sharing information is under-utilized. Moreover, the issue of contribution evaluation is still unsolved. In this paper, we simultaneously solve the former two challenges (i.e., model convergence and contribution evaluation) by modeling the indirect data sharing of FL as a problem of information communication. We prove that FL with non-iid data forms noisy communication channels, which have limited capability for information transmission, i.e., limited channel capacity. The main factor in deciding the channel capacity is the Gradient Signal to Noise Ratio (GSNR). Through analyzing GSNR, we further prove that channel capacity can be reached by optimal local updates and propose a method FedGSNR to calculate it, which allows us to maximize channel utilization in FL, leading to faster model convergence. Moreover, as the contribution of the local dataset depends on the amount of provided information, the derived GSNR allows the server to accurately evaluate the contributions of different clients (i.e., the quality of local datasets).

When workers are heterogeneous in computing and transmission capabilities, the global efficiency of federated learning suffers from the straggler issue, i.e., the slowest worker drags down the overall training process. We propose a novel and efficient federated learning framework named FedPAGE, where workers perform distributed pruning adaptively towards global efficiency, i.e., fast training and high accuracy. For fast training, we develop a pruning rate learning approach generating an adaptive pruning rate for each worker, making the overall update time approximate to the fastest worker’s update time, i.e., no stragglers. For high accuracy, we find that structural similarity between sub-models is essential to global model accuracy in the distributed pruning, and thus propose the CIG_X pruning scheme to ensure maximum similarity. Meanwhile, we adopt the sparse training and design model aggregating of different size sub-models to cope with distributed pruning. We prove the convergence of FedPAGE and demonstrate the effectiveness of FedPAGE on image classification and natural language inference tasks. Compared with the state-of-the-art, FedPAGE achieves higher accuracy with the same speedup ratio.

The past decade has witnessed the rapid expansion of demands for mobile traffic, while the traditional mobile traffic pricing schemes cannot accommodate such demands. Sponsored data plan (SDP), which can increase the revenue of all stakeholders in the market through transferring some of the revenue from content providers (CPs) to end users (EUs), is more suitable. However, existing studies have focused more on Internet service providers (ISPs) and CPs, ignoring the influence of EUs (e.g., the inherent attribute differences of EUs and the interaction among EUs) on the market under SDP. Regarding the difficulty of modeling the abstract property about interaction among EUs, we utilize network congestion as the medium and construct the congestion-aware SDP model based on Stackelberg game. The newly proposed model can not only analyze how network congestion affects SDP mechanism, but also elucidate the impact of interactions among EUs. More specifically, through theoretical analysis, we prove that there is a unique dynamic equilibrium in the interaction among EUs (i.e., the traffic consumption of different EUs). By taking into account network congestion, the newly proposed model also more accurately and realistically describes the optimal strategies and computation methods of all stakeholders in the market. Moreover, simulation experiments demonstrate that the positive effect brought by SDP is not as obvious as before, and EUs influence each other instead of being independent of each other. Overall, this paper emphasizes the non-negligible influence of EUs and promotes a deeper understanding of SDP mechanism, which can guide the relevant stakeholders to optimize their own decision-making details.

Due to the explosive growth of social network services, friendship inference has been widely adopted by Online Social Service Providers (OSSPs) for friend recommendation. The conventional techniques, however, have limitations in accuracy or scalability to handle such a large yet sparse multi-source data. For example, the OSSPs will be required to manually give the order in which the various information is applied. This unavoidably reduces the applicability of existing friend recommendation systems. To address this issue, we propose a Two-stage Deep learning framework for Friendship Inference (TDFI). This approach can utilize multi-source information simultaneously with low complexity. In particular, we apply an Extended Adjacency Matrix (EAM) to represent the multi-source information. We then adopt an improved Deep AutoEncoder Network (iDAEN) to extract the fused feature vector for each user. The TDFI framework also provides an improved Deep Siamese Network (iDSN) to measure user similarity from iDAEN. Finally, we evaluate the effectiveness and robustness of TDFI on three large-scale real-world datasets. It shows that TDFI can effectively handle the sparse multi-source data while providing better accuracy for friend recommendation.