RC-SSFL: Towards Robust and Communication-efficient Semi-supervised Federated Learning System

Federated Learning (FL) is an emerging decentralized artificial intelligence paradigm, which promises to train a shared global model in high-quality while protecting user data privacy. However, the current systems rely heavily on a strong assumption: all clients have a wealth of ground truth labeled data, which may not be always feasible in the real life. In this paper, we present a practical Robust, and Communication-efficient Semi-supervised FL (RC-SSFL) system design that can enable the clients to jointly learn a high-quality model that is comparable to typical FL's performance. In this setting, we assume that the client has only unlabeled data and the server has a limited amount of labeled data. Besides, we consider malicious clients can launch poisoning attacks to harm the performance of the global model. To solve this issue, RC-SSFL employs a minimax optimization-based client selection strategy to select the clients who hold high-quality updates and uses geometric median aggregation to robustly aggregate model updates. Furthermore, RC-SSFL implements a novel symmetric quantization method to greatly improve communication efficiency. Extensive case studies on two real-world datasets demonstrate that RC-SSFL can maintain the performance comparable to typical FL in the presence of poisoning attacks and reduce communication overhead by $2 \times \sim 4 \times$

A Survey on Federated Unlearning: Challenges, Methods, and Future Directions

In recent years, the notion of ``the right to be forgotten" (RTBF) has evolved into a fundamental element of data privacy regulations, affording individuals the ability to request the removal of their personal data from digital records. Consequently, given the extensive adoption of data-intensive machine learning (ML) algorithms and increasing concerns for personal data privacy protection, the concept of machine unlearning (MU) has gained considerable attention. MU empowers an ML model to selectively eliminate sensitive or personally identifiable information it acquired during the training process. Evolving from the foundational principles of MU, federated unlearning (FU) has emerged to confront the challenge of data erasure within the domain of federated learning (FL) settings. This empowers the FL model to unlearn an FL client or identifiable information pertaining to the client while preserving the integrity of the decentralized learning process. Nevertheless, unlike traditional MU, the distinctive attributes of federated learning introduce specific challenges for FU techniques. These challenges lead to the need for tailored design when designing FU algorithms. Therefore, this comprehensive survey delves into the techniques, methodologies, and recent advancements in federated unlearning. It provides an overview of fundamental concepts and principles, evaluates existing federated unlearning algorithms, reviews optimizations tailored to federated learning, engages in discussions regarding practical applications, along with an assessment of their limitations, and outlines promising directions for future research

Leia: A Lightweight Cryptographic Neural Network Inference System at the Edge

The advances in machine learning have revealed its great potential for emerging mobile applications such as face recognition and voice assistant. Models trained via a Neural Network (NN) can offer accurate and efficient inference services for mobile users. Unfortunately, the current deployment of such service encounters privacy concerns. Directly offloading the model to the mobile device violates model privacy of the model owner, while feeding user input to the service compromises user privacy. To address this issue, we propose, tailor, and evaluate Leia, a lightweight cryptographic NN inference system at the edge. Unlike prior cryptographic NN inference systems, Leia is designed with two mobile-friendly perspectives. First, Leia leverages the paradigm of edge computing wherein the inference procedure keeps the model closer to the mobile user to foster low latency service. Specifically, Leia\u27s architecture consists of two non-colluding edge services to obliviously perform NN inference on the encoded user data and model. Second, Leia\u27s realization makes the judicious use of potentially constrained computational and communication resources in edge devices. In particular, Leia adapts the Binarized Neural Network (BNN), a trending flavor of NN model with low memory footprint and computational cost, and purely chooses the lightweight secret sharing techniques to develop secure blocks of BNN. Empirical validation executed on Raspberry Pi confirms the practicality of Leia, showing that Leia can produce a prediction result with 97% accuracy by 4 seconds in the edge environment

Deep Learning-Based Medical Diagnostic Services: A Secure, Lightweight, and Accurate Realization

In this paper, we propose CryptMed, a system framework that enables medical service providers to offer secure, lightweight, and accurate medical diagnostic service to their customers via an execution of neural network inference in the ciphertext domain. CryptMed ensures the privacy of both parties with cryptographic guarantees. Our technical contributions include: 1) presenting a secret sharing based inference protocol that can well cope with the commonly-used linear and non-linear NN layers; 2) devising an optimized secure comparison function that can efficiently support comparison-based activation functions in NN architectures; 3) constructing a suite of secure smooth functions built on precise approximation approaches for accurate medical diagnoses. We evaluate CryptMed on 6 neural network architectures across a wide range of non-linear activation functions over two benchmark and four real-world medical datasets. We comprehensively compare our system with prior art in terms of end-to-end service workload and prediction accuracy. Our empirical results demonstrate that CryptMed achieves up to respectively $413\times$, $19\times$, and $43\times$ bandwidth savings for MNIST, CIFAR-10, and medical applications compared with prior art. For the smooth activation based inference, the best choice of our proposed approximations preserve the precision of original functions, with less than 1.2\% accuracy loss and could enhance the precision due to the newly introduced activation function family

Evidence for gill slits and a pharynx in Cambrian vetulicolians: implications for the early evolution of deuterostomes.

BACKGROUND: Vetulicolians are a group of Cambrian metazoans whose distinctive bodyplan continues to present a major phylogenetic challenge. Thus, we see vetulicolians assigned to groups as disparate as deuterostomes and ecdysozoans. This divergence of opinions revolves around a strikingly arthropod-like body, but one that also bears complex lateral structures on its anterior section interpreted as pharyngeal openings. Establishing the homology of these structures is central to resolving where vetulicolians sit in metazoan phylogeny. RESULTS: New material from the Chengjiang Lagerstätte helps to resolve this issue. Here, we demonstrate that these controversial structures comprise grooves with a series of openings. The latter are oval in shape and associated with a complex anatomy consistent with control of their opening and closure. Remains of what we interpret to be a musculature, combined with the capacity for the grooves to contract, indicate vetulicolians possessed a pumping mechanism that could process considerable volumes of seawater. Our observations suggest that food captured in the anterior cavity was transported to dorsal and ventral gutters, which then channeled material to the intestine. This arrangement appears to find no counterpart in any known fossil or extant arthropod (or any other ecdysozoan). Anterior lateral perforations, however, are diagnostic of deuterostomes. CONCLUSIONS: If the evidence is against vetulicolians belonging to one or other group of ecdysozoan, then two phylogenetic options seem to remain. The first is that such features as vetulicolians possess are indicative of either a position among the bilaterians or deuterostomes but apart from the observation that they themselves form a distinctive and recognizable clade current evidence can permit no greater precision as to their phylogenetic placement. We argue that this is too pessimistic a view, and conclude that evidence points towards vetulicolians being members of the stem-group deuterostomes; a group best known as the chordates (amphioxus, tunicates, vertebrates), but also including the ambulacrarians (echinoderms, hemichordates), and xenoturbellids. If the latter, first they demonstrate that these members of the stem group show few similarities to the descendant crown group representatives. Second, of the key innovations that underpinned deuterostome success, the earliest and arguably most seminal was the evolution of openings that define the pharyngeal gill slits of hemichordates (and some extinct echinoderms) and chordates.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Tree species richness differentially affects the chemical composition of leaves, roots and root exudates in four subtropical tree species

Plants produce thousands of compounds, collectively called the metabolome, which mediate interactions with other organisms. The metabolome of an individual plant may change according to the number and nature of these interactions. We tested the hypothesis that tree diversity level affects the metabolome of four subtropical tree species in a biodiversity–ecosystem functioning experiment, BEF‐China. We postulated that the chemical diversity of leaves, roots and root exudates increases with tree diversity. We expected that the strength of this diversity effect differs among leaf, root and root exudates samples. Considering their role in plant competition, we expected to find the strongest effects in root exudates. Roots, root exudates and leaves of four tree species ( Cinnamomum camphora , Cyclobalanopsis glauca , Daphniphyllum oldhamii and Schima superba ) were sampled from selected plots in BEF‐China. The exudate metabolomes were normalized over their non‐purgeable organic carbon level. Multivariate analyses were applied to identify the effect of both neighbouring (local) trees and plot diversity on tree metabolomes. The species‐ and sample‐specific metabolites were assigned to major compound classes using the ClassyFire tool, whereas potential metabolites related to diversity effects were annotated manually. Individual tree species showed distinct leaf, root and root exudate metabolomes. The main compound class in leaves was the flavonoids, whereas carboxylic acids, prenol lipids and specific alkaloids were most prominent in root exudates and roots. Overall, plot diversity had a stronger effect on metabolome profiles than the local diversity. Leaf metabolomes responded more often to tree diversity level than exudates, whereas root metabolomes varied the least. We found no uniform or general pattern of alterations in metabolite richness or diversity in response to variation in tree diversity. The response differed among species and tissues. Synthesis . Classification of metabolites supported initial ecological interpretation of differences among species and organs. Particularly, the metabolomes of leaves and root exudates respond to differences in tree diversity. These responses were neither linear nor uniform and individual metabolites showed different dynamics. More controlled interaction experiments are needed to dissect the causes and consequences of the observed shifts in plant metabolomes