Tootfinder

Advancing Practical Homomorphic Encryption for Federated Learning: Theoretical Guarantees and Efficiency Optimizations
Ren-Yi Huang, Dumindu Samaraweera, Prashant Shekhar, J. Morris Chang
https://arxiv.org/abs/2509.20476

Advancing Practical Homomorphic Encryption for Federated Learning: Theoretical Guarantees and Efficiency Optimizations
Federated Learning (FL) enables collaborative model training while preserving data privacy by keeping raw data locally stored on client devices, preventing access from other clients or the central server. However, recent studies reveal that sharing model gradients creates vulnerability to Model Inversion Attacks, particularly Deep Leakage from Gradients (DLG), which reconstructs private training data from shared gradients. While Homomorphic Encryption has been proposed as a promising defense me…

An Empirical Analysis of Secure Federated Learning for Autonomous Vehicle Applications
Md Jueal Mia, M. Hadi Amini
https://arxiv.org/abs/2509.20223 https://

An Empirical Analysis of Secure Federated Learning for Autonomous Vehicle Applications
Federated Learning lends itself as a promising paradigm in enabling distributed learning for autonomous vehicles applications and ensuring data privacy while enhancing and refining predictive model performance through collaborative training on edge client vehicles. However, it remains vulnerable to various categories of cyber-attacks, necessitating more robust security measures to effectively mitigate potential threats. Poisoning attacks and inference attacks are commonly initiated within the f…

Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
Wenxuan Ye, Xueli An, Onur Ayan, Junfan Wang, Xueqiang Yan, Georg Carle
https://arxiv.org/abs/2508.13625

Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
Large models, renowned for superior performance, outperform smaller ones even without billion-parameter scales. While mobile network servers have ample computational resources to support larger models than client devices, privacy constraints prevent clients from directly sharing their raw data. Federated Learning (FL) enables decentralized clients to collaboratively train a shared model by exchanging model parameters instead of transmitting raw data. Yet, it requires a uniform model architectur…

Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments
Saeid Sheikhi, Panos Kostakos, Lauri Loven
https://arxiv.org/abs/2509.18044

Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments
Federated Learning (FL) in 5G and edge network environments face severe security threats from adversarial clients. Malicious participants can perform label flipping, inject backdoor triggers, or launch Sybil attacks to corrupt the global model. This paper introduces Hybrid Reputation Aggregation (HRA), a novel robust aggregation mechanism designed to defend against diverse adversarial behaviors in FL without prior knowledge of the attack type. HRA combines geometric anomaly detection with momen…

Som anarkosyndikalist finner jeg stor inspirasjon i det Norsk Syndikalistisk Forbund (NSF-IAA) har stått for helt siden stiftelsen i desember 1916. Forbundet har fra begynnelsen representert en tydelig og kompromisslŸs tradisjon innen arbeiderbevegelsen, der prinsippene om selvorganisering, solidaritet og direkte aksjon har stått i sentrum.
NSF-IAA har gjennom mer enn hundre år vært en stemme for en fagbevegelse som ikke underordner seg partipolitikk eller byråkratiske strukturer, men …

We can be happy that they turned the federation to the fediverse off, when they forked Trumps TruthSocial.
If they would bridge their Bsky accounts, it will be hard to block. You can't then block a complete server.
Source: @… on bsky:

I think he is trying to say he is an Avenger without realising he is the supervillain.
Also, does he know that while he is torrenting the world libraries into his bank account, you could set up for $10 a month your own Mastodon instance which is ten times better federated than his 'federated' Threads?

Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach
Jundong Chen, Honglei Zhang, Chunxu Zhang, Fangyuan Luo, Yidong Li
https://arxiv.org/abs/2508.12386

Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach
Federated recommendation (FR) facilitates collaborative training by aggregating local models from massive devices, enabling client-specific personalization while ensuring privacy. However, we empirically and theoretically demonstrate that server-side aggregation can undermine client-side personalization, leading to suboptimal performance, which we term the aggregation bottleneck. This issue stems from the inherent heterogeneity across numerous clients in FR, which drives the globally aggregated…

Jeg vet ikke hva alle andre mener, men jeg synes Norsk Syndikalistisk Forbund – Internasjonale Arbeider-Assosiasjon (NSF-IAA) er en veldig viktig og bra organisasjon. De bygger videre på anarkosyndikalistiske tradisjoner i Norge som går helt tilbake til 1916. NSF-IAA står for en kampvillig, medlemsstyrt og frigjŸrende arbeiderbevegelse som utfordrer både byråkrati, partipolitikk og kapitalmakt.
De gir et ekte alternativ til de tradisjonelle fagforeningene som ofte er preget av kompromi…

On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions
Daniel M. Jimenez-Gutierrez, Yelizaveta Falkouskaya, Jose L. Hernandez-Ramos, Aris Anagnostopoulos, Ioannis Chatzigiannakis, Andrea Vitaletti
https://arxiv.org/abs/2508.13730

On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions
Federated Learning (FL) is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While FL enhances data privacy by design, it remains vulnerable to various security and privacy threats. This survey provides a comprehensive overview of more than 200 papers regarding the state-of-the-art attacks and defense mechanisms developed to address these challenges, categorizing them into security-enhancing and pr…