Robust federated learning with noisy communication

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs

Cyclin B1/Cdk1 phosphorylation of mitochondrial p53 induces anti-apoptotic response.

The pro-apoptotic function of p53 has been well defined in preventing genomic instability and cell transformation. However, the intriguing fact that p53 contributes to a pro-survival advantage of tumor cells under DNA damage conditions raises a critical question in radiation therapy for the 50% human cancers with intact p53 function. Herein, we reveal an anti-apoptotic role of mitochondrial p53 regulated by the cell cycle complex cyclin B1/Cdk1 in irradiated human colon cancer HCT116 cells with p53(+/+) status. Steady-state levels of p53 and cyclin B1/Cdk1 were identified in the mitochondria of many human and mouse cells, and their mitochondrial influx was significantly enhanced by radiation. The mitochondrial kinase activity of cyclin B1/Cdk1 was found to specifically phosphorylate p53 at Ser-315 residue, leading to enhanced mitochondrial ATP production and reduced mitochondrial apoptosis. The improved mitochondrial function can be blocked by transfection of mutant p53 Ser-315-Ala, or by siRNA knockdown of cyclin B1 and Cdk1 genes. Enforced translocation of cyclin B1 and Cdk1 into mitochondria with a mitochondrial-targeting-peptide increased levels of Ser-315 phosphorylation on mitochondrial p53, improved ATP production and decreased apoptosis by sequestering p53 from binding to Bcl-2 and Bcl-xL. Furthermore, reconstitution of wild-type p53 in p53-deficient HCT116 p53(-/-) cells resulted in an increased mitochondrial ATP production and suppression of apoptosis. Such phenomena were absent in the p53-deficient HCT116 p53(-/-) cells reconstituted with the mutant p53. These results demonstrate a unique anti-apoptotic function of mitochondrial p53 regulated by cyclin B1/Cdk1-mediated Ser-315 phosphorylation in p53-wild-type tumor cells, which may provide insights for improving the efficacy of anti-cancer therapy, especially for tumors that retain p53

Throughput improvement for multi-hop UAV relaying

Unmanned aerial vehicle (UAV) relaying is one of the main technologies for UAV communications. It uses UAVs as relays in the sky to provide reliable wireless connection between remote users. In this paper, we consider a multi-hop UAV relaying system. To improve the spectrum efficiency of the system, we maximize the average end-to-end throughput from the source to the destination by jointly optimizing the bandwidth allocated to each hop, the transmit power for the source and relays, and the trajectories of the UAVs, subject to constraints on the total spectrum bandwidth, the average and peak transmit power, the UAV mobility and collision avoidance, and the information-causality of multi-hop relaying. The formulated optimization is non-convex. We propose an efficient algorithm to approximate and solve it, using the alternating optimization and successive convex optimization methods. Numerical results show that the proposed optimization significantly outperforms other benchmark schemes, verifying the effectiveness of our scheme

Molecular basis for heat desensitization of TRPV1 ion channels.

The transient receptor potential vanilloid 1 (TRPV1) ion channel is a prototypical molecular sensor for noxious heat in mammals. Its role in sustained heat response remains poorly understood, because rapid heat-induced desensitization (Dh) follows tightly heat-induced activation (Ah). To understand the physiological role and structural basis of Dh, we carried out a comparative study of TRPV1 channels in mouse (mV1) and those in platypus (pV1), which naturally lacks Dh. Here we show that a temperature-sensitive interaction between the N- and C-terminal domains of mV1 but not pV1 drives a conformational rearrangement in the pore leading to Dh. We further show that knock-in mice expressing pV1 sensed heat normally but suffered scald damages in a hot environment. Our findings suggest that Dh evolved late during evolution as a protective mechanism and a delicate balance between Ah and Dh is crucial for mammals to sense and respond to noxious heat

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm

Privacy preservation via beamforming for NOMA

Non-orthogonal multiple access (NOMA) has been proposed as a promising multiple access approach for 5G mobile systems because of its superior spectrum efficiency. However, the privacy between the NOMA users may be compromised due to the transmission of a superposition of all users’ signals to successive interference cancellation (SIC) receivers. In this paper, we propose two schemes based on beamforming optimization for NOMA that can enhance the security of a specific private user while guaranteeing the other users’ quality of service (QoS). Specifically, in the first scheme, when the transmit antennas are inadequate, we intend to maximize the secrecy rate of the private user, under the constraint that the other users’ QoS is satisfied. In the second scheme, the private user’s signal is zero-forced at the other users when redundant antennas are available. In this case, the transmission rate of the private user is also maximized while satisfying the QoS of the other users. Due to the nonconvexity of optimization in these two schemes, we first convert them into convex forms and then, an iterative algorithm based on the ConCave-Convex Procedure is proposed to obtain their solutions. Extensive simulation results are presented to evaluate the effectiveness of the proposed scheme