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Št. zadetkov: 5
Client clustering for improved federated learning on heterogeneous data
Boris Radovič, Veljko Pejović
Magistrsko delo
Oznake: machine learning;deep learning;federated learning;computer science;master's thesis;
Federated learning (FL) is a distributed machine learning paradigm in which a model is collectively trained by using the data available on multiple devices without such devices exposing their data. This concept marks a significant stride towards decentralized AI. However, the challenge arises when d ...
Leto: 2023 Vir: Fakulteta za računalništvo in informatiko (UL FRI)
Review and comparative evaluation of resource-adaptive collaborative training for heterogeneous edge devices
Boris Radovič, Marco Canini, Veljko Pejović
Izvirni znanstveni članek
Oznake: zvezno učenje;porazdeljeno učenje;vseprisotno računalništvo;mobilno računalništvo;federated learning;split learning;distributed collaborative learning;ubiquitous and mobile computing;device heterogeneity;
Growing concerns about centralized mining of personal data threatens to stifle further proliferation of machine learning (ML) applications. Consequently, a recent trend in ML training advocates for a paradigm shift – moving the computation of ML models from a centralized server to a federation of ed ...
Leto: 2025 Vir: Fakulteta za računalništvo in informatiko (UL FRI)
Analysis and usage of API gateways in Kubernetes
Boris Radovič, Matjaž B. Jurič, Basel Magableh
Diplomsko delo
Oznake: API gateway;microservices;Kubernetes;Strimzi;Apache Kafka;computer science;computer and information science;diploma thesis;
Containerization has marked a turning point in the way software gets developed: applications' functionalities, instead of being bundled inside of single and potentially very large code bases as is the case in monolith architectures, are split into smaller units, the microservices precisely, and even ...
Leto: 2020 Vir: Fakulteta za računalništvo in informatiko (UL FRI)
Train your cake and eat it too! Repurposing collaborative training to tailor LLMs to private data without sharing
Boris Radovič, Mohammed Aljahdali, Marco Canini, Veljko Pejović, Zuhair Khayyat
Objavljeni znanstveni prispevek na konferenci
Oznake: poraba energije;digitalni podatki;energy usage;digital data;
In the emerging field of large language models (LLMs), a significant challenge arises when organizations with vast datasets lack the computational resources to independently train and fine-tune models. This issue stems from privacy, compliance, and resource constraints: organizations cannot share th ...
Leto: 2024 Vir: Fakulteta za računalništvo in informatiko (UL FRI)
Towards a unified framework for split learning
Boris Radovič, Marco Canini, Samuel Horváth, Veljko Pejović, Praneeth Vepakomma
Objavljeni znanstveni prispevek na konferenci
Oznake: porazdeljeno učenje;strojno učenje;zvezno učenje;split learning;machine learning;federated learning;
Split Learning (SL) is a principled approach for training models on data distributed across multiple devices without sharing training data. While SL emerged as an alternative to federated learning to reduce the compute burden on devices, it also enables a more fair redistribution of work between edg ...
Leto: 2025 Vir: Fakulteta za računalništvo in informatiko (UL FRI)
Št. zadetkov: 5
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