315 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
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