Federated learning (FL) is a privacy-preserving distributed paradigm that enables clients to jointly train global model. In real-world FL implementations, client data could have label noise, and different vastly noise levels. Although there exist methods in centralized for tackling such do not perform well on heterogeneous settings, due the typically smaller sizes of datasets privacy requirements FL. this paper, we propose FedCorr, general multi-stage framework tackle FL, without making any...

We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, reduces the difficult process of novel problem-solving to routine computation. The fundamental objects interest are ideals some suitably initialized polynomial ring. shall explain how solving Raven's Progressive Matrices (RPMs) can be realized as computational problems in algebra, which combine various well-known subroutines include: Computing Gröbner basis an ideal,...

Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying diverse performance requirements local clients simultaneously. Existing PFL methods are inherently based on idea that relations between global and personalized models captured by similarity weights. Such primarily either partitioning architecture into versus components, or modeling client relationships via To...

In this paper, a transmission strategy of fountain codes over cooperative relay networks is proposed. When more than one nodes are available, we apply network coding to fountain-coded packets. By doing this, partial information made available the destination node about upcoming message block. It therefore able reduce required number transmissions erasure channels, hence increasing effective throughput. Its application wireless channels with Rayleigh fading and AWGN noise also analysed,...

Abstract Web image datasets curated online inherently contain ambiguous in-distribution instances and out-of-distribution instances, which we collectively call non-conforming (NC) instances. In many recent approaches for mitigating the negative effects of NC core implicit assumption is that can be found via entropy maximization. For “entropy” to well-defined, are interpreting output prediction vector an instance as parameter a multinomial random variable, with respect some trained model...

In this paper, we propose a technique to improve the performance of Joint Channel and Network Coding (JCNC) scheme in Multiple Access Relay (MARC). This is motivated by observation that decoding quality improved when channel output can be processed iteratively. Using different permutation patterns for users, enabled more scenarios destination apply iterative decoding, thereby achieving an improvement overall performance. The proposed evaluated Gaussian channels Rayleigh fading channels, it...

The universal approximation theorem, in one of its most general versions, says that if we consider only continuous activation functions $\sigma$, then a standard feedforward neural network with hidden layer is able to approximate any multivariate function $f$ given threshold $\varepsilon$, and $\sigma$ non-polynomial. In this paper, give direct algebraic proof the theorem. Furthermore shall explicitly quantify number units required for approximation. Specifically, $X\subseteq \mathbb{R}^n$...

In this paper, a novel transmission scheme which incorporates network coding into rateless over relay networks is proposed. This technique allows partial information about the upcoming message block to be transmitted during current transmission, hence reducing required number of transmissions and improving spectral efficiency. Different techniques for applying several fading models are discussed, their relationships an erasure channel model explained. An optimisation weight given, we show...

In this paper, a transmission strategy of fountain codes over cooperative relay networks is proposed. When more than one nodes are available, we apply network coding to fountain-coded packets. By doing this, partial information made available the destination node about upcoming message block. It therefore able reduce required number transmissions erasure channels, hence increasing effective throughput. Its application wireless channels with Rayleigh fading and AWGN noise also analysed,...

Foundation models (FMs) are general-purpose artificial intelligence (AI) that have recently enabled multiple brand-new generative AI applications. The rapid advances in FMs serve as an important contextual backdrop for the vision of next-generation wireless networks, where federated learning (FL) is a key enabler distributed network intelligence. Currently, exploration interplay between and FL still its nascent stage. Naturally, capable boosting performance FL, could also leverage...

Leveraging over-the-air computations for model aggregation is an effective approach to cope with the communication bottleneck in federated edge learning. By exploiting superposition properties of multi-access channels, this facilitates integrated design and computation, thereby enhancing system privacy while reducing implementation costs. However, inherent electromagnetic interference radio channels often exhibits heavy-tailed distributions, giving rise exceptionally strong noise globally...

This paper presents the outage probability analysis of Joint Channel and Network Coding (JCNC) scheme in Multiple Access Relay (MARC). In particular, effect using different interleaver patterns at users is analysed. The motivation this work to show that despite its capability achieving up 2 dB gain Packet Error Rate (PER), corresponding improvement only minimal (upper bounded approximately 0.6 dB). signifies achieved by largely attributed ability iteratively decodable codes perform closer...

For classification tasks, deep neural networks are prone to overfitting in the presence of label noise. Although existing methods able alleviate this problem at low noise levels, they encounter significant performance reduction high or even medium levels when is asymmetric. To train classifiers that universally robust all and not sensitive any variation model, we propose a distillation-based framework incorporates new subcategory Positive-Unlabeled learning. In particular, shall assume small...

Fountain codes are rateless erasure-correcting codes. Several fountain have been proposed recently to minimize overhead, many of which involve modifications the Luby transform (LT) code. These codes, like LT code, implicit assumption that probability distribution is fixed throughout encoding process. In this paper, we will use theory posets show unnecessary, and by dropping it, can achieve overhead reduction as much 64% lower than We also present fundamental designs for with non-constant...

A colored complex of type $\mathbf{a} = (a_1, \dots, a_n)$ is a simplicial $Δ$ on vertex set $V$, together with an ordered partition $(V_1, V_n)$ such that every face $F$ satisfies $|F \cap V_i| \leq a_i$. For each $\mathbf{b} (b_1, b_n) \mathbf{a}$, let $f_{\mathbf{b}}$ be the number faces b_i$. The array integers $\{f_{\mathbf{b}}\}_{\mathbf{b} \mathbf{a}}$ called fine $f$-vector $Δ$, and it refinement $Δ$. In this paper, we generalize notion Macaulay representations give numerical...

This paper evaluates the performance of FASTAR code in Multimedia Broadcast Multicast Service (MBMS) System. It is demonstrated that code, which optimised for erasure channel through its overhead minimising degree distribution, can improve system compared to conventional systematic Raptor code. The simulated under Evolved UMTS Terrestrial Radio Access (E-UTRA) network environment according 3GPP LTE-A specifications. extent improvement and relation parameters are also studied.

Federated learning (FL) offers a solution to train global machine model while still maintaining data privacy, without needing access stored locally at the clients. However, FL suffers performance degradation when client distribution is non-IID, and longer training duration combat this may not necessarily be feasible due communication limitations. To address challenge, we propose new adaptive algorithm $\texttt{AdaFL}$, which comprises two components: (i) an attention-based selection...