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| 2. |
- Dinh, Canh T., et al.
(författare)
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A New Look and Convergence Rate of Federated Multitask Learning With Laplacian Regularization
- 2023
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Ingår i: IEEE Transactions on Neural Networks and Learning Systems. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2162-237X .- 2162-2388.
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Tidskriftsartikel (refereegranskat)abstract
- Non-independent and identically distributed (non-IID) data distribution among clients is considered as the key factor that degrades the performance of federated learning (FL). Several approaches to handle non-IID data, such as personalized FL and federated multitask learning (FMTL), are of great interest to research communities. In this work, first, we formulate the FMTL problem using Laplacian regularization to explicitly leverage the relationships among the models of clients for multitask learning. Then, we introduce a new view of the FMTL problem, which, for the first time, shows that the formulated FMTL problem can be used for conventional FL and personalized FL. We also propose two algorithms FedU and decentralized FedU (dFedU) to solve the formulated FMTL problem in communication-centralized and decentralized schemes, respectively. Theoretically, we prove that the convergence rates of both algorithms achieve linear speedup for strongly convex and sublinear speedup of order 1/2 for nonconvex objectives. Experimentally, we show that our algorithms outperform the conventional algorithm FedAvg, FedProx, SCAFFOLD, and AFL in FL settings, MOCHA in FMTL settings, as well as pFedMe and Per-FedAvg in personalized FL settings.
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| 3. |
- Vu, Tung T., et al.
(författare)
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Data Size-Aware Downlink Massive MIMO: A Session-Based Approach
- 2022
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Ingår i: IEEE Wireless Communications Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2162-2337 .- 2162-2345. ; 11:7, s. 1468-1472
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Tidskriftsartikel (refereegranskat)abstract
- This letter considers the development of transmission strategies for the downlink of massive multiple-input multiple-output networks, with the objective of minimizing the completion time of the transmission. Specifically, we introduce a session-based scheme that splits time into sessions and allocates different rates in different sessions for the different users. In each session, one user is selected to complete its transmission and will not join subsequent sessions, which results in successively lower levels of interference when moving from one session to the next. An algorithm is developed to assign users and allocate transmit power that minimizes the completion time. Numerical results show that our proposed session-based scheme significantly outperforms conventional non-session-based schemes.
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| 4. |
- Vu, Thanh Tung, et al.
(författare)
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Energy-Efficient Massive MIMO for Federated Learning : Transmission Designs and Resource Allocations
- 2022
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Ingår i: IEEE Open Journal of the Communications Society. - : Institute of Electrical and Electronics Engineers (IEEE). - 2644-125X. ; 3, s. 2329-2346
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Tidskriftsartikel (refereegranskat)abstract
- This work proposes novel synchronous, asynchronous, and session-based designs for energy-efficient massive multiple-input multiple-output networks to support federated learning (FL). The synchronous design relies on strict synchronization among users when executing each FL communication round, while the asynchronous design allows more flexibility for users to save energy by using lower computing frequencies. The session-based design splits the downlink and uplink phases in each FL communication round into separate sessions. In this design, we assign users such that one of the participating users in each session finishes its transmission and does not join the next session. As such, more power and degrees of freedom will be allocated to unfinished users, resulting in higher rates, lower transmission times, and hence, higher energy efficiency. In all three designs, we use zero-forcing processing for both uplink and downlink, and develop algorithms that optimize user assignment, time allocation, power, and computing frequencies to minimize the energy consumption at the base station and users, while guaranteeing a predefined maximum execution time of each FL communication round.
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| 5. |
- Vu, Tung T., et al.
(författare)
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Energy-Efficient Massive MIMO for Serving Multiple Federated Learning Groups
- 2021
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Ingår i: 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM). - : IEEE. - 9781728181042
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Konferensbidrag (refereegranskat)abstract
- With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a learning framework that suits beyond SG and towards 6G systems. This work looks into a future scenario in which there are multiple groups with different learning purposes and participating in different FL processes. We give energy-efficient solutions to demonstrate that this scenario can be realistic. First, to ensure a stable operation of multiple FL processes over wireless channels, we propose to use a massive multiple-input multiple-output network to support the local and global FL training updates, and let the iterations of these FL processes be executed within the same large-scale coherence time. Then, we develop asynchronous and synchronous transmission protocols where these iterations are asynchronously and synchronously executed, respectively, using the downlink unicasting and conventional uplink transmission schemes. Zero-forcing processing is utilized for both uplink and downlink transmissions. Finally, we propose an algorithm that optimally allocates power and computation resources to save energy at both base station and user sides, while guaranteeing a given maximum execution time threshold of each FL iteration. Compared to the baseline schemes, the proposed algorithm significantly reduces the energy consumption, especially when the number of base station antennas is large.
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