3 citations · 6 across the 15 of their papers we have counts for
15 papers
CARVY-FL: Client Anticlustering for Robust Voting in Provably Secure Federated Learning
Masaki Nakada, Honoka Anada, Tatsuya Kaneko +3
Federated learning (FL) enables collaborative training without directly sharing raw data, but remains vulnerable to malicious clients. Voting-based FL improves robustness by partit…
Clutch: High Performance Vector-Scalar Comparison using DRAM via Chunked Temporal Coding
Daichi Tokuda, Tatsuya Kubo, Ismail Emir Yuksel +8
Vector-scalar comparison is a fundamental computation primitive that compares each element in a vector against a single scalar value. It is widely used in various data-intensive wo…
PuDGhost: Experimental Analysis of Computation Result Corruption in Processing-using-DRAM Operations on Real DRAM Chips and Implications for Future Systems
Daichi Tokuda, İsmail Emir Yüksel, Tatsuya Kubo +7
Processing-using-DRAM (PuD) is a promising computation paradigm that alleviates frequent data movement between main memory and processing units by using each DRAM column as a compu…
Relational Hoare Logic for High-Level Synthesis of Hardware Accelerators
Izumi Tanaka, Ken Sakayori, Shinya Takamaeda-Yamazaki +1
High-level synthesis (HLS) is a powerful tool for developing efficient hardware accelerators that rely on specialized memory systems to achieve sufficient on-chip data reuse and of…
PUDTune: Multi-Level Charging for High-Precision Calibration in Processing-Using-DRAM
Tatsuya Kubo, Daichi Tokuda, Lei Qu +2
Recently, practical analog in-memory computing has been realized using unmodified commercial DRAM modules. The underlying Processing-Using-DRAM (PUD) techniques enable high-through…
How to Evaluate Participant Contributions in Decentralized Federated Learning
Honoka Anada, Tatsuya Kaneko, Shinya Takamaeda-Yamazaki
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without sharing local data. In particular, decentralized FL (DFL), where clients e…