85 research outputs found
Towards Sybil Resilience in Decentralized Learning
Federated learning is a privacy-enforcing machine learning technology but
suffers from limited scalability. This limitation mostly originates from the
internet connection and memory capacity of the central parameter server, and
the complexity of the model aggregation function. Decentralized learning has
recently been emerging as a promising alternative to federated learning. This
novel technology eliminates the need for a central parameter server by
decentralizing the model aggregation across all participating nodes. Numerous
studies have been conducted on improving the resilience of federated learning
against poisoning and Sybil attacks, whereas the resilience of decentralized
learning remains largely unstudied. This research gap serves as the main
motivator for this study, in which our objective is to improve the Sybil
poisoning resilience of decentralized learning.
We present SybilWall, an innovative algorithm focused on increasing the
resilience of decentralized learning against targeted Sybil poisoning attacks.
By combining a Sybil-resistant aggregation function based on similarity between
Sybils with a novel probabilistic gossiping mechanism, we establish a new
benchmark for scalable, Sybil-resilient decentralized learning.
A comprehensive empirical evaluation demonstrated that SybilWall outperforms
existing state-of-the-art solutions designed for federated learning scenarios
and is the only algorithm to obtain consistent accuracy over a range of
adversarial attack scenarios. We also found SybilWall to diminish the utility
of creating many Sybils, as our evaluations demonstrate a higher success rate
among adversaries employing fewer Sybils. Finally, we suggest a number of
possible improvements to SybilWall and highlight promising future research
directions
Survey on social reputation mechanisms: Someone told me I can trust you
Nowadays, most business and social interactions have moved to the internet,
highlighting the relevance of creating online trust. One way to obtain a
measure of trust is through reputation mechanisms, which record one's past
performance and interactions to generate a reputational value. We observe that
numerous existing reputation mechanisms share similarities with actual social
phenomena; we call such mechanisms 'social reputation mechanisms'. The aim of
this paper is to discuss several social phenomena and map these to existing
social reputation mechanisms in a variety of scopes. First, we focus on
reputation mechanisms in the individual scope, in which everyone is responsible
for their own reputation. Subjective reputational values may be communicated to
different entities in the form of recommendations. Secondly, we discuss social
reputation mechanisms in the acquaintances scope, where one's reputation can be
tied to another through vouching or invite-only networks. Finally, we present
existing social reputation mechanisms in the neighbourhood scope. In such
systems, one's reputation can heavily be affected by the behaviour of others in
their neighbourhood or social group.Comment: 10 pages, 3 figures, 1 tabl
Augmenting LLMs with Knowledge: A survey on hallucination prevention
Large pre-trained language models have demonstrated their proficiency in
storing factual knowledge within their parameters and achieving remarkable
results when fine-tuned for downstream natural language processing tasks.
Nonetheless, their capacity to access and manipulate knowledge with precision
remains constrained, resulting in performance disparities on
knowledge-intensive tasks when compared to task-specific architectures.
Additionally, the challenges of providing provenance for model decisions and
maintaining up-to-date world knowledge persist as open research frontiers. To
address these limitations, the integration of pre-trained models with
differentiable access mechanisms to explicit non-parametric memory emerges as a
promising solution. This survey delves into the realm of language models (LMs)
augmented with the ability to tap into external knowledge sources, including
external knowledge bases and search engines. While adhering to the standard
objective of predicting missing tokens, these augmented LMs leverage diverse,
possibly non-parametric external modules to augment their contextual processing
capabilities, departing from the conventional language modeling paradigm.
Through an exploration of current advancements in augmenting large language
models with knowledge, this work concludes that this emerging research
direction holds the potential to address prevalent issues in traditional LMs,
such as hallucinations, un-grounded responses, and scalability challenges
Web3Recommend: Decentralised recommendations with trust and relevance
Web3Recommend is a decentralized Social Recommender System implementation
that enables Web3 Platforms on Android to generate recommendations that balance
trust and relevance. Generating recommendations in decentralized networks is a
non-trivial problem because these networks lack a global perspective due to the
absence of a central authority. Further, decentralized networks are prone to
Sybil Attacks in which a single malicious user can generate multiple fake or
Sybil identities. Web3Recommend relies on a novel graph-based content
recommendation design inspired by GraphJet, a recommendation system used in
Twitter enhanced with MeritRank, a decentralized reputation scheme that
provides Sybil-resistance to the system. By adding MeritRank's decay parameters
to the vanilla Social Recommender Systems' personalized SALSA graph algorithm,
we can provide theoretical guarantees against Sybil Attacks in the generated
recommendations. Similar to GraphJet, we focus on generating real-time
recommendations by only acting on recent interactions in the social network,
allowing us to cater temporally contextual recommendations while keeping a
tight bound on the memory usage in resource-constrained devices, allowing for a
seamless user experience. As a proof-of-concept, we integrate our system with
MusicDAO, an open-source Web3 music-sharing platform, to generate personalized,
real-time recommendations. Thus, we provide the first Sybil-resistant Social
Recommender System, allowing real-time recommendations beyond classic
user-based collaborative filtering. The system is also rigorously tested with
extensive unit and integration tests. Further, our experiments demonstrate the
trust-relevance balance of recommendations against multiple adversarial
strategies in a test network generated using data from real music platforms
Mass Adoption of NATs: Survey and experiments on carrier-grade NATs
In recent times, the prevalence of home NATs and the widespread
implementation of Carrier-Grade NATs have posed significant challenges to
various applications, particularly those relying on Peer-to-Peer communication.
This paper addresses these issues by conducting a thorough review of related
literature and exploring potential techniques to mitigate the problems. The
literature review focuses on the disruptive effects of home NATs and CGNATs on
application performance. Additionally, the study examines existing approaches
used to alleviate these disruptions. Furthermore, this paper presents a
comprehensive guide on how to puncture a NAT and facilitate direct
communication between two peers behind any type of NAT. The techniques outlined
in the guide are rigorously tested using a simple application running the IPv8
network overlay, along with their built-in NAT penetration procedures. To
evaluate the effectiveness of the proposed techniques, 5G communication is
established between two phones using four different Dutch telephone carriers.
The results indicate successful cross-connectivity with three out of the four
carriers tested, showcasing the practical applicability of the suggested
methods.Comment: 12 pages, 9 figure
- …