This paper studies the information theoretic secure aggregation problem in a three-layer hierarchical network with arbitrary heterogeneous data assignment, where clustered users communicate with an aggregation server through an intermediate layer of relays. We consider a more general setting with arbitrary heterogeneous data assignment across users, where `arbitrary' means that the data assignment is given in advance and `heterogeneous' means that the users may hold different numbers of datasets. Each user locally computes the partially aggregated gradients as its input based on the assigned datasets and transmits masked input to its associated relay. The relays then forward the aggregated messages to the server, which aims to recover the sum of the gradients. In this process, while some users may drop out unpredictably, the server needs to correctly recover the desired aggregation from the surviving users. Moreover, the server or any relay may collude with a subset of users. We impose the following security constraints: (i) server security, requiring the server to learn only the sum of gradients without gaining any additional information about individual inputs; and (ii) relay security, ensuring that each relay learns nothing about users' inputs. Under these constraints, we propose an aggregation scheme that guarantees information theoretic security and achieves the optimal two-layer communication loads.

Distributed linearly separable computation is a fundamental problem in large-scale distributed systems, requiring the computation of linearly separable functions over different datasets across distributed workers. This paper studies a heterogeneous distributed linearly separable computation problem, including one master and N distributed workers. The linearly separable task function involves Kc linear combinations of K messages, where each message is a function of one dataset. Distinguished from the existing homogeneous settings that assume each worker holds the same number of datasets, where the data assignment is carefully designed and controlled by the data center (e.g., the cyclic assignment), we consider a more general setting with arbitrary heterogeneous data assignment across workers, where `arbitrary' means that the data assignment is given in advance and `heterogeneous' means that the workers may hold different numbers of datasets. Our objective is to characterize the fundamental tradeoff between the computable dimension of the task function and the communication cost under arbitrary heterogeneous data assignment. Under the constraint of integer communication costs, for arbitrary heterogeneous data assignment, we propose a universal computing scheme and a universal converse bound by characterizing the structure of data assignment, where they coincide under some parameter regimes. We then extend the proposed computing scheme and converse bound to the case of fractional communication costs.

The design of target-specific molecules such as small molecules, peptides, and antibodies is vital for biological research and drug discovery. Existing generative methods are restricted to single-domain molecules, failing to address versatile therapeutic needs or utilize cross-domain transferability to enhance model performance. In this paper, we introduce Unified generative Modeling of 3D Molecules (UniMoMo), the first framework capable of designing binders of multiple molecular domains using a single model. In particular, UniMoMo unifies the representations of different molecules as graphs of blocks, where each block corresponds to either a standard amino acid or a molecular fragment. Based on these unified representations, UniMoMo utilizes a geometric latent diffusion model for 3D molecular generation, featuring an iterative full-atom autoencoder to compress blocks into latent space points, followed by an E(3)-equivariant diffusion process. Extensive benchmarks across peptides, antibodies, and small molecules demonstrate the superiority of our unified framework over existing domain-specific models, highlighting the benefits of multi-domain training.

This paper considers a multi-message secure aggregation with demand privacy problem, in which a server aims to compute$K_{c} \geq 1$linear combinations of local inputs from$K$distributed and non-colluding users. The problem addresses two tasks: (1) security, ensuring that the server can only obtain the desired linear combinations without any else information about the users' inputs, and (2) privacy, preventing users from learning about the server's computation task. In addition, the effect of user dropouts is considered, where at most$K-U$users can drop out and the identity of these users cannot be predicted in advance. We propose two schemes for$\mathrm{K}_{\mathrm{c}}=1$and$2 \leq \mathrm{K}_{\mathrm{c}}<\mathrm{U}$, respectively. For$\mathrm{K}_{\mathrm{c}}=1$, we introduce multiplicative encryption of the server's demand using a random variable, where users share coded keys offline and transmit masked models in the first round, followed by aggregated coded keys in the second round for task recovery. For$2 \leq \mathrm{K}_{\mathrm{c}}<\mathrm{U}$, we use robust symmetric private computation to recover linear combinations of keys in the second round. The objective is to minimize the number of symbols sent by each user during the two rounds. Our proposed schemes have achieved the optimal rate region when$\mathrm{K}_{\mathrm{c}}=1$and the order optimal rate (within 2) when$2 \leq \mathrm{K}_{\mathrm{c}}<\mathrm{U}$.

Cyclic peptides exhibit better binding affinity and proteolytic stability compared to their linear counterparts. However, the development of cyclic peptide design models is hindered by the scarcity of data. To address this, we introduce **CPSea**(**C**yclic **P**eptide **Sea**), a dataset of 2.71 million cyclic peptide-receptor complexes, curated through systematic mining of the AlphaFold Database (AFDB). Our pipeline extracts compact domains from AFDB, identifies cyclization sites using the $\beta$-carbon (C$_\beta$) distance thresholds, and applies multi-stage filtering to ensure structure fidelity and binding compatibility. Compared with experimental data of cyclic peptides, CPSea shows similar distributions in metrics on structure fidelity and wet-lab compatibility. To our knowledge, CPSea is the largest cyclic peptide-receptor dataset to date, enabling end-to-end model training for the first time. The dataset also showcases the feasibility of simulating inter-chain interactions using intra-chain interactions, expanding available resources for machine-learning models on protein-protein interactions. The dataset and relevant scripts are accessible on GitHub ([https://github.com/YZY010418/CPSea](https://github.com/YZY010418/CPSea)).

This paper studies information theoretic secure aggregation in federated learning, involving K distributed users and a central server. “Secure” means that the server can only get aggregated locally trained model updates, with no other information about the local users' data being leaked to the server. In addition, the effect of user dropouts is considered, where at most$\mathsf{K}-\mathsf{U}$users can drop and the identity of these users cannot be predicted in advance. Users share keys in an offline way independently of the models, and send the encrypted models to the server in the model aggregation phase. The objective of this problem is to minimize the number of transmissions in the model aggregation phase. A secure aggregation scheme with uncoded groupwise keys, where any$\mathsf{S}$users share an independent key, was recently proposed to achieve the same optimal communication cost as the best scheme with coded keys when$\mathsf{S} > \mathsf{K}-\mathsf{U}$. In this paper, we additionally consider the potential impact of user collusion, where up to$\mathsf{T}$users may collude with the server. For this setting, we propose a secure aggregation scheme with uncoded groupwise keys that guarantees secure aggregation with$\mathsf{U}$non-dropped users and$\mathsf{T}$colluding users provided that$\mathsf{K}-\mathsf{U}+1\leq \mathsf{S}\leq$K - T, and is proven to achieve the optimality without any constraint on the keys.

With the rapid growth of mobile internet, Internet of Things (IoT), and cloud computing, the demand for data services has arisen sharply. As the core data service infrastructure, the number of data centers (DCs) has surged and led to higher energy consumption, which is not conducive to energy conservation, emission reduction, and sustainable development. In this paper, we proposed an energy-saving solution based on Artificial Intelligence (AI) and digital twin in DC scenarios, called Smart DC. The proposed solution can reduce DCs' energy consumption by optimizing air distribution and reducing cooling redundancy. Specifically, the digital twin model in this solution is used to verify and optimize AI strategies, and solve the problem of insufficient data in physical data center. Data for AI training and information mining is limited because the environment in the DCs change little. Moreover, in order to ensure that the DCs operate at a safe temperature, the adjustment of parameters should be conservative, so there is still room for cooling redundancy. We combined digital twin and AI, exploring the temperature rise boundary in the digital DCs and mine more data pairs, which has proven to increase the robustness of the AI model and achieve better energy-saving effect. The simulation and experiment results show that the proposed solution can ensure safe and efficient operation and keep the energy-saving rate of the cooling system to reach 41.07%.

With the fast developing of Health-IoT supported by massive Machine Type Communication (mMTC) of 5G, personal physiological data of patients endure various malicious attacks from intelligent eavesdropper during the transmission procedure. The leakage of patients' data will threaten their personal information security. In this paper, we propose a hybrid Relay Selection and Cooperative Jamming (RS-CJ) scheme to resist intelligent eavesdroppers in the sensor network tier in Healthcare-IoT systems. In the hybrid RS-CJ scheme, the relay is adaptively selected from bio-sensors before the physical data are transmitted, and the unselected bio-sensors act as friendly jammers. Moreover, we derived the security capacity of the legitimate and eavesdropping channels under the RS-CJ scheme. Then, we obtained the closed-form expressions of the Ergodic Achievable Security Rate (EASR). We also analyzed the negative influence on EASR when the intelligent eavesdropper holds different active jamming power. The simulation results verify the correctness of EASR, and our proposed RS-CJ scheme is feasible in general Healthcare-IoT systems. Compared with related solutions, EASR of the proposed hybrid RS-CJ scheme is 64% higher than the existing relay selection and interference scheme, as well as 3.38 times higher than that of the optional relay selection scheme proposed by other scholars.

As the deepening of 5G's support for the e-health industry, more and more wireless medical devices will suffer from various attacks and threats. Especially, the security of implantable medical devices (IMDs) which have limited computational capabilities and stringent power constraints becomes a critical issue. According to the channel state information, we exploit the special characteristics of the received signal strength (RSS) ratio between wearable proxy devices (WPDs) and IMDs in wireless body area networks (WBANs) to distinguish legitimate users and attackers. Moreover, based on the idea of proposed authentication request filtering (ARF), we design two corresponding light-weight security protocols to defend the forced authentication (FA) attacks and enhance the accessibility of IMD in emergency mode respectively. Simulation results show that the proposed ARF scheme to defend FA attacks achieves a high authentication response rate (ARR) with 99.2% for legitimate users and a low ARR with 2.4% for attackers at the maximum gap threshold point. Furthermore, when applied in emergency mode, the ARF scheme allows up to 96.3% emergency rescue devices to access the IMDs with only one attempt.